{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--TABLE OF CONTENTS-->\n",
    "Contents:\n",
    "- [Decision trees](#Decision-trees)\n",
    "  - [Visualizing decision tree](#Visualizing-decision-tree)\n",
    "  - [Visualizing decision boundary](#Visualizing-decision-boundary)\n",
    "  - [Hyperparameter tuning for trees](#Hyperparameter-tuning-for-trees)\n",
    "  - [Feature importance](#Feature-importance)\n",
    "  - [Decision tree for regression](#Decision-tree-for-regression)\n",
    "  - [Practice question](#Practice-question)\n",
    "- [Ensemble Methods](#Ensemble-Methods)\n",
    "  - [Voting classifiers](#Voting-classifiers)\n",
    "  - [Bagging](#Bagging)\n",
    "    - [Out-of-Bag evaluation](#Out-of-Bag-evaluation)\n",
    "    - [Random Forest](#Random-Forest)\n",
    "  - [Boosting](#Boosting)\n",
    "    - [Gradient Boosting](#Gradient-Boosting)\n",
    "    - [Histogram-Based Gradient Boosting](#Histogram-Based-Gradient-Boosting)\n",
    "    - [XGBoost](#XGBoost)\n",
    "    - [LightGBM](#LightGBM)\n",
    "    - [CatBoost](#CatBoost)\n",
    "- [Imbalanced classification](#Imbalanced-classification)\n",
    "  - [Under-sampling](#Under-sampling)\n",
    "  - [Over-sampling](#Over-sampling)\n",
    "  - [SMOTE](#SMOTE)\n",
    "    - [Numerical variables](#Numerical-variables)\n",
    "    - [Categorical variables](#Categorical-variables)\n",
    "    - [Mixed data types](#Mixed-data-types)\n",
    "  - [Adjusting weights of the minority class](#Adjusting-weights-of-the-minority-class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "from sklearn.inspection import DecisionBoundaryDisplay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Part of this section is from: https://jakevdp.github.io/PythonDataScienceHandbook/05.08-random-forests.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will use a decision tree for classification of a small synthetic dataset. As we have done before, we will use `make_blobs` method from `sklearn` to generate our data. \n",
    "`make_blobs()` has a parameter *n_features* (the number of features for each sample), and its default value is 2.\n",
    "We will be creating our data *X* with 2 features, and *y* will correspond to the label of the cluster to which a point belongs. \n",
    "Then, we will visualize our data with scatter plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "X, y = make_blobs(n_samples=300, centers=4, random_state=0, cluster_std=1)\n",
    "fig = plt.figure(figsize=(3,3))\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we have used one additional parameter in the scatter plot, *cmap*, which defined the colormap. Here are some different choices for the color scheme: \n",
    "https://matplotlib.org/stable/tutorials/colors/colormaps.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's confirm the size of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the distribution of classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 75, 3: 75, 0: 75, 2: 75})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will fit a decision tree for classification.\n",
    "The full list of the parameters is available in the documentation: \\\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html\n",
    "\n",
    "\n",
    "Note that by default, the criterion for finding the best splits is Gini index.\n",
    "The parameter which controls this is \n",
    "*criterion* , default=*gini*, it can be set to *entropy*.\n",
    "Let's first plot a smaller tree for easier visualization, we will set the depth of the tree as 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=3)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "tree = DecisionTreeClassifier(max_depth=3)\n",
    "tree.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision classifier has an attribute called *tree_* which allows access to low level attributes such as *node_count*,\n",
    "the total number of nodes, and *max_depth*, the maximal depth of the tree. It also stores the entire binary tree structure,\n",
    "More details in:\n",
    "\n",
    "https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html\n",
    "    \n",
    "Let's see the total number of nodes and the depth of a tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of nodes is  9\n",
      "depth of the tree is  3\n"
     ]
    }
   ],
   "source": [
    "print('number of nodes is ', tree.tree_.node_count)\n",
    "print('depth of the tree is ', tree.tree_.max_depth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing decision tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now see our constructed tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plot_tree(tree, class_names=['0','1','2','3'], filled=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each box in the plot represents a node. Let's look at the root node output.\n",
    "\n",
    "First line of the output: X[1]>=6.6261 is the first split. Based on the result, it either follows the true (left) or the false (right) path.\n",
    "\n",
    "*gini = 0.75*: The gini score is a metric that quantifies the purity of the node/leaf. A gini score greater than zero implies that the samples contained within that node belong to different classes. A gini score of 0 means that the node is pure, that within that node only a single class of data points exist. \n",
    "Notice that we have a gini score greater than zero. Therefore, we know that the data points contained within the root node belong to different classes.\n",
    "\n",
    "*samples = 300*: Since our dataset set contains 300  data points, this value is set to 300.\n",
    "\n",
    "*value = [75, 75, 75, 75]*: The value list tells us how many samples at the given node fall into each class. The first element of the list shows the number of points that belong to class 0, the second element of the list shows the number of points that belong to class 1,... Notice how this node is not a pure one since different types of classes are contained within the same node. We knew this already from the gini score.\n",
    "\n",
    "*class* = 0: The class value shows the prediction a given node will make and it can be determined from the value list. Whichever class occurs the most within the node will be selected as the class value. If the decision tree were to end at the root node, it would predict that all 300 samples belonged to class 0. \n",
    "Of course this makes no sense, since there is an equal number of samples for each class, but the decision tree is programmed to choose the first class on the list if there is an equal number of samples for each class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for this exercise, we are not splitting the data into train and test. \n",
    "We will not be evaluating the classifier performance on the test set, or tuning the hyperparameters.\n",
    "The goal of this part is only to visualize and understand the tree performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing decision boundary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will use the visualize the decision regions just like we did for the other classifiers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now see our classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Tree of depth 3')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "ax = plt.axes()\n",
    "disp = DecisionBoundaryDisplay.from_estimator(tree, X, response_method=\"predict\", alpha=0.5, ax=ax)\n",
    "scatter=ax.scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\")\n",
    "handles, labels = scatter.legend_elements()\n",
    "ax.legend(handles=handles, labels=[\"Class \" + str(k) for k in range(0,4)], frameon=False)\n",
    "ax.set_title(\"Decision Tree of depth 3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the decision boundaries are perpendicular to the axes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(20, 5))\n",
    "\n",
    "for depth, ax in zip(range(4,9,2), axs):\n",
    "        tree = DecisionTreeClassifier(max_depth=depth)\n",
    "        tree.fit(X, y)\n",
    "        disp = DecisionBoundaryDisplay.from_estimator(tree, X, response_method=\"predict\", alpha=0.5, ax=ax)\n",
    "        scatter=ax.scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\")\n",
    "        handles, labels = scatter.legend_elements()\n",
    "        ax.legend(handles=handles, labels=[\"Class \" + str(k) for k in range(0,4)], frameon=False)\n",
    "        ax.set_title(f\"Decison tree of depth {depth}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize decision boundaries for trees of varying depth."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that as the depth increases, we tend to get very strangely shaped classification regions; \n",
    "for example, for depth 4, there is a tall and skinny purple region between the green and blue regions.\n",
    "It's clear that this is less a result of the true data distribution, and more a result of the particular sampling or noise properties of the data.\n",
    "That is, this decision tree, even at only 4 levels deep, is clearly over-fitting our data. Such over-fitting turns out to be a general property of decision trees: it is very easy to go too deep in the tree, and thus to fit details of the particular data rather than the overall properties of the distributions they are drawn from. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to see this over-fitting is to look at models trained on different subsets of the data, for example, if we train two different trees, each on half of the original data.\n",
    "\n",
    "Recall that on numpy arrays slicing (taking elements from one given index to another given index) is done [start:end:step], so taking every other element is done as X[::2] (equivalent to X[0::2]), starting from the first element, index 0. Remember that the second element has index 1.\n",
    "\n",
    "If we do not specify the maximum depth of a tree, the default value is *None*, meaning that the nodes are expanded until all leaves are pure or until all leaves contain less than *min_samples_split* samples (which has a default value of 2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision Tree trained on the other half of the data')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mAXTvUAkJadxk5LliGIbJKNhm/HXYZvy7bcb0QLZR0uuctkvHR6X8GXGuM8omywx7lq4dWTZUWtC60muGoLJWkmhJOgYqtZXa5zSPdB3R2Gm9jLKfaLvUsIeuPyqblULyB3TtUnl0cHAwMprM/k0hvS/oWk96n1J5cFLbN6N+UyR9npAOKo2TyuIJtnUzB25mwmQYZDSRDglBGiIUACIdGnrJgh4upJdAmitt27b9pX3Rg33RokUYO3as0GOhBwVppJBGDRl1aUFfpAUKFEjmtCHoISl9PylJDQpCagD+iqGZFgoKCkJ3JSlk8JDxJJ3HtIRzaT1C+iBPCTmh/tTokwVpvpCOBXXkos5USaFxp/xiSMnP5lN6XFLdD1nQfkg3gzqD0blMCX25k6HzI6TnmdZNCX1Z0pd2UsgwlOpmJB37z66DtPZDX5ZkLKS83uh6SPlDh/ZDuhxSaI5IAD7leFJeIz8iPfshSIdp2bJlQjeKHFA/uz5+B5oDMl5TkvSeJH2/jCQ95zO996IspOc1reszpUFz+vRpzJ07V/zoSaoJk9aP3pTQvbBgwQKhB0U/phOC7wmk58cYwzBMVsM246/BNuPfbzOmh/TOye+e64yyyTLDnv1V2zPlXMk6D+S0WrVqldAS//LlSzIdSdJ7zghIQ5Kuf1nXD80rjcHV1VVoL2Ykmf2bIi1bl4IpSR2iGfWbgpyLpF144MCBVONiWzdzYEchkyG4ubmJm5SyWAh66BEkEJtWtE667u9CWVotWrQQDQMoUkjGJf1Ypqh0mTJlkBFQBEQWSX+I/y5Jo1hSaN7I4COxXllIH+TS+d2zZ49MI5cMyj9BVrc6Ms4pQktG0fLly4XYLTm8yMBasWJF4pj+ZD6l21iyZAlKly6dZoTsV5p2ZARpjTur9pP0eqM5ItFlOgeyoPOSEfshkXmKClIElESV6bqkz9E9RtdCbiY95zO99+KfQqLP1P2zZs2awlClCDMZWeT0s7e3T9c2hg8fLtanZ2KVKlXEjy8yDjt16pSu+5JhGCYrYZvx12Gb8Tv/ss2Y3jnJrHOdnfbsr3ZIT88Y5s+fL54F1BRjzpw5ooEL3Wf0vMjt9lNW/aZIDxnxm4Iyzu/cuSM+T/c73ds0fmpklNvPVU6FHYVMhkBfSITUwJNGEugHb/369X/4WeqU9PLly9/aL32WIsT0oqgIPTgoWpGyk54U6iZFkRR6oCQ1uCi6IX0/O6HjoXLVatWq/fALkdYj6EH7s/mVRXozlZJy6tQpYWxRB8KkUbo/LVmRdVwU8fzRcZHxS/MjjZwm5d27dz/dj/Q807opo660LKOug6T7SRpdo3Jkilz+zrmjOaJubGSA/855TC/UdZfGfOzYsWT7oeyAjITmSNY5+5N7MiPmJb33oiykY07P9UlldJR9QD9c6YegFHL8pfe46FxRRgU9+5KWZaTsrM0wDJMTYJsxY2Cb8e+yGTPCfvnTc51emyyj7KyssGd/BNlP1BV427ZtyZaT/USdrTMCuv6o63Ba80rPlt9xyGX3OUhq6ya9LyhbkH7jkETEr/6mSGsMlAXq6OgoMgqpe7IUWfc0k3GwRiHzx1A0lqIwlDrctWvXxC8oah2/adMmodclKw1bCpWQ0EPq+PHj6Y7CUgo3/RBO+bDT1NT8YeSwadOm8PLywsGDBxOXkUbCmjVrRGSC9C+yE4qWUNo7zWdKaJzSH/5kXJNhRJGwpOnbsuZXFtSWnvgVR4I0MpWyrFGWQ+N3IV0dOo9Lly4VZUlpHReNheaAMgNcXFwS36f0eXK4/AzSxKBrdOPGjcmul3PnzoltkO5MRkBGGkXQV69enWzeyCChufud/dA1QuWlW7ZsSfUeldaQNk9GIOt8379/X5QQZSR0Tz548CDZdukYNm/eDBsbm1/So5FCBhnxJ46y9N6LsqCsQPoBSmUWScshSDsopb4PzTMZRknLXUgHiK5tWfetrP3SNlI+K+mZlnSbDMMwOQG2GTMOthn/LpvxR9/z6eVPz3V6bbLfuSayy579EbLsJ9Ltk2qiZtQ+GjZsCAcHB2HfSfH29haVI6QH+Tvl/9l9Dui+ICco3ReUACFl586dqcaU3t8Uadnvsj5PrFy5Ml3HyfwenFHI/BL0pUjRDzJA6AFHBh/9+KWoAkUNk4r8rlu3Tjz8KKWZmjdQJIE+Qw8FKjshQ4+gFGKKNLRv316kftMXP+kQ0Pbo4ZM0IiHl/fv3IvpBDzj60qJUejIaaftUbpcWJMZLhiilPz9+/Fh86dG+SUeFHjZkNGYnZHQOHDhQpGKTXhl9sVCEnSIm9MVFOhrt2rUTXygbNmxA9+7dUbZsWXHM9LAmA4hElSm6vHbt2jT3Q04MeuiSZg8ZbpTJRNGgtHRuCBoLOb2odIfGSEYZfbHQZ2QZ9r8DRdW2bt2KJk2aCK0OErImTSL6EqMoNB03RakJiiqdP39eCBWTwLTUeKfPpdTaSwnNKR07bZ/mnESf6dqh+aVrYvTo0RlyPHROJk+eLMZKqfFUYkoRRSoxrVChQjKR4fRC5/zQoUNCPJjmhM41/VCg+5KWk9FLX95/Cuk3UeSPBOPJCKboIN2PdL/JMsh/l0mTJmH//v3inJNAPJV9kION9kfZdilLrdIDZQ7QOOnHXcGCBcU2SVPnV7QO03svpgV9juaNnoH0XKNnmvT6TDp/tA6VfND1QaLWpLtCz04qs0t5HdOzkbJHaH0zMzPxQ5u0hOhcUYYOlRzTcdMzltbLKH0dhmGY34FtxsyFbca/y2Yk6Hqmc0W6xWQH0HylpTcoiz891+m1ychBSw3T6J6j+4CcVmSP/IqOYFbZsz+C7KfZs2eLc1u1alW8ePFClPLL0tj7E+h80rOPnnF0/dEziJ4t5HhevHjxb20zu88B3Rd0XHR/0zVKzZzoOqFgQMr5S+9vih/Z7yTRQ3NFDnC6z6lBDG2HyUQysIMy8xcjbQEvfVGLd2qx3qBBA8mqVaskwcHBMj9HbeB79Ogh1lVUVJSYm5tLmjdvLjly5Eiy9fz8/CTDhg0T79O2LSwsJD179pR8+/ZNZlt6Wj506FBJ4cKFJerq6hJtbW1JpUqVJIcOHUq2XWrNnrQ9O+Ht7S3p3bu3xMDAQOyrRIkSqVrL/6g9fVpt29OCtkGfoW1KoWOjcacFtZYvV66cRFVVVaKpqSnGOGHCBImHh0ey9a5evSpp1KiROH4VFRWJra2tpFevXpJHjx79dFxbtmyR5MuXTyIvLy/GR9sirK2tJc2aNZP5mZMnT0pKliwp9mVjYyNZtGiRZPv27amOL+W807ZpncOHDyfbXsrzKuXp06eSNm3aSPT19SXKyspiTB06dJA4OjomW+/69etinug80rFs3LhRnJv0PtoOHjwoKVOmjNiHnp6epGvXrhI3N7dk66R1rn5lP2vXrhXXKt0DxsbGksGDB0sCAgKSrUPzVaxYsVSfpf3T8SclOjpazD2tT2PX1dUV8zBr1ixJUFDQD8eS3v3Ex8dL5s+fL5bRPmieTp8+LXM8Ke+JtM53WtBzol27dhIdHR1xbVWsWFHsKyW0Tbrv08OdO3cSr42k4/vV85nee1EWR48elRQpUkTMX9GiRSXHjh2TOX/btm2TFChQQKxH1wndD7LG8/btW0nNmjXFWOg92hZB15L0maahoSGeCbQu7Ue6DsMwTFbBNuN32GZkm5FI7368vLzEfNJ5pPWl8yK9px4+fJhsfelcSc9HRpzr9NpkDg4OwrZRUFBIdl4yw579FfvvR9dlymstMjJSMnbsWImpqam4f6pVqya5e/duqvVkXXtpnZO0ePLkiTgnZKepqalJ6tSpI2zV9D5LZJGV5yAt1q9fL8mbN6/4bPny5SU3btxINX+/8psiLfud7rXWrVuL65Ku6/bt24tn3K8+Y5n0I0f/yUxHJMMwDMMwDMMwDMMwDMMwOR/WKGQYhmEYhmEYhmEYhmEYhh2FDMMwDMMwDMMwDMMwDMOwo5BhGIZhGIZhGIZhGIZhGHYUMgzDMAzDMAzDMAzDMAxDsKOQYRiGYRiGYRiGYRiGYRh2FDIMwzAMwzAMwzAMwzAMAyggBxMfHw8PDw9oampCTk4uu4fDMAzDMAzzy0gkEoSEhMDMzAx58nCMNrfB9ijDMAzDMP+SPZqjHYVklFlaWmb3MBiGYRiGYf4YV1dXWFhYZPcwmF+E7VGGYRiGYf4lezRHOwopcks4vZoKTU2V7B4OwzAMwzDMLxMSEolSxeYl2jVM7oLtUYZhGIZh/iV7NEc7CqXlHWSUaWqxYcYwDMMwTO6Fy1ZzJ2yPMgzDMAzzL9mjLJTDMAzDMAzDMAzDMAzDMAw7ChmGYRiGYRiGYRiGYRiGYUchwzAMwzAMwzAMwzAMwzDsKGQYhmEYhmEYhmEYhmEYhmBHIcMwDMMwDMMwDMMwDMMw7ChkGIZhGIZhGIZhGIZhGIYdhQzDMAzDMAzDMAzDMAzDsKOQYRiGYRiGYRiGYRiGYRiCHYUMwzAMwzAMwzAMwzAMw7CjkGEYhmEYhmEYhmEYhmEYdhQyDMMwDMMwDMMwDMMwDMOOQoZhGIZhGIZhGIZhGIZhCHYUMgzDMAzDMAzDMAzDMAzDjkKGYRiGYRiGYRiGYRiGYQCF7B4Aw/xrPHnsgt077+HTRx9oaKiiZauSaN22NFRUFLN7aAzDMAzDMMw/gK9vKPbuvo+b1z8gLi4eFSrlRc9elWFppZvdQ2MYhmGyGc4oZJgsQiKRYPqUk2hUbw1uX3OCrZk/YsNdMGLoIdStuRwe7oHZPUSGYRiGYRjmL+fWjY+oVHYBli+5CH11L5jr+2LHluuoVG4hjh97lt3DYxiGYbIZzihkmCyCsgg3rr+JlXMMMaS3NuTl5cTyl2+j0KKbF3p224mLV0ZCTi5hOcMwDMMwDMNkJN7ewejeZQcqlVXA/g0W0NeTF8tDw+IxeIIPhgzYj4IFjVCsuFl2D5VhGIbJJjijkGGygPj4eGxYew0d7DQxvJ9OopOQKF5YGZuXGeDZU3fcuf05W8fJMAzDMAzD/L3s2Xkf8XGxOLjJJNFJSGio58H2lcYwMZLHlk23snWMDMMwTPbCjkKGyQJcXQLx6ZM/urbVkPl+/ZpqMDFSxPVrH7J8bAzDMAzDMMy/wbUr79CsgRp0db47CaUoKsqho506rl99ly1jYxiGYXIG7ChkmCwgLj5e/KuiLPuWo3JjZSU5xMYmrMcwDMMwDMMwmWGTqiinLXNDtio1N2EYhmH+XX7bUXjjxg20aNECZmZmwslx4sSJVI0bZsyYAVNTU6iqqqJ+/fr48IGzpZh/E0tLXRgZqcPhfKjM9588j8RXt2iUL2+V5WNjGIZhmNwM26QMk37KlrPGOccIREWldgbSvXLiXBjKlrfJlrExDMMwudxRGBYWhlKlSmHdunUy31+8eDFWr16NjRs34v79+1BXV0ejRo0QGRn5J+NlmFyJoqI8evWpiq37gnHpeliy9wIC4zBk0jdYWmqjYeMi2TZGhmEYhsmNsE3KMOmnT7+q8A+Mw6jpvoiLkyRzEs5fGYBX76LQr3+1bB0jwzAMk0u7Hjdp0kS8ZEFfNCtXrsS0adNgZ2cnlu3evRvGxsYiytupU6ffHzHD5FJGjqmLx4++oknn92hURx21qqjAzTMW9sfCADkFHD7eCwoKqfViGIZhGIZJG7ZJGSb92OY3xPJV7TBm5BFcuRUlNAmVFIGjZ8Px/FUkJk1piOo182f3MBmGYZi/TaPwy5cv8PLyEqUdUrS1tVGpUiXcvXs3zc9FRUUhODg42Yth/haUlBSwZ38frFjdHr7Buli0LgwnLwFdulfDlRtjUKq0RXYPkWEYhmH+Kn7HJmV7lPnb6dq9Is5dGoaSZYtg875IrNoWATOrvDhyoj/GTmiQ3cNjGIZhcmtG4Y8gg4ygaG1S6G/pe7JYsGABZs2alRlDYpgcU4JMxhm9GIZhGIbJXH7HJmV7lPkXKFvOCpu2dc3uYTAMwzA5kBzV9Xjy5MkICgpKfLm6umb3kBiGYRiGYZh/CLZHGYZhGIb5l8kUR6GJiYn419vbO9ly+lv6niyUlZWhpaWV7MUwDMMwDMMwWWWTsj3KMAzDMMy/TKY4CvPmzSuML0dHx8RlpO9CneaqVKmSGbtkGIZhGIZhmGSwTcowDMMwDJNFGoWhoaH4+PFjMrHoZ8+eQU9PD1ZWVhg1ahTmzp2LAgUKCCNt+vTpMDMzQ6tWrX53lwzDZDERETFwdwuAmpoSzMx1sns4DMMwDJMKtkkZ5u+Gupe7ugQgJjYOlpa6okEgwzAMk3n89lP20aNHqFOnTuLfY8aMEf/27NkTO3fuxIQJExAWFoYBAwYgMDAQ1atXx/nz56GiopIxI2cYJtMIDAzHovkXcdD+IUJCo8Wy0mXMMWpMPTRrUSK7h8cwDMMwibBNyjB/r4Nw/75HWLfmCt6/+yaW6eurolvPKhg7vj5UVRWze4gMwzB/JXISegLnUKg0RFtbG59d5kBTi405hskKggIj0LLpOni4f8OQXlqoX1MNvv5x2LInGJdvhGPR0tbo069qdg+TYRgm1xASHIl8VtNFYwzWu8t9sD3KMNnD/LnnsWKpI1o31UDPjppQV8uD0xfDsHlvMMqWs8bBowOgrMzZhQzDMBltj/KTlWGYZKxaeRWuLt9w57Q5ihZSTlzetpkGRkz1xbTJDmhhVxKGhhrZOk6GYRiGYRjm7+TNay/hJJw3WR+TRuglLq9bXQ1tmmmgbltn7N55D/0HVs/WcTIMw/yNZEozE4ZhcidxcfGw33MPfTprJnMSEnJycpg1Xh/y8sAB+4fZNkaGYRiGYRjm72bPrvswMVLEmEG6qd6rXkkVrZqoY8/Ou9kyNoZhmL8dzihkmFzEV2d/fP3qBx0dNZQoaSacdxlJYGAE/PwiUKOy7MYlerryKFFEGZ8+JujEMAzDMAzDMP8WoSGReP7cQ/x/yZJm0NDM+JL8Tx99ULmcEpSUZNu6taqo4tRFvwzfL8MwDMOOQobJFbx+5YnpUxxw4/qnxGUFChpg4pTGsGtVKsP2Q6LQefLIwcMrVub78fESeHrHolyV5NmGDMMwDMMwzN9NREQM5s46C/s99xEaFiOWaWoooUv3Spj2XxOoqGRccxFyPrp/jUvzfTfPWGhqKmXY/hiGYZjvcOkxw+Rw3r7xQosm6+Dn5YZda4zx/q41Lh4yRyGbCPTrtRf2ezOuDFhNTQkNGhbGpj3BiIlJ3efo5IUwuHnEwK51xjknGYZhGIZhmJxNbGwcenTZjj277mDUAE04XbUSrxH9NLB7521077xdrJNRtGxVEg+fRuLhs8hU74WExmP3oVC0bFU6w/bHMAzDfIczChkmhzNrxmmYGQG3TplDS1NeLLO1UULd6qroM8pbZBqS405dPWOiqqPG1kPLpu/Qvr8Xlv6nj/x5lYTT8OiZUAyZ6Is69QqgXHkrZBXeXkHYueMezp56DpevAdDWUUXrtmWEeLWZuewSaYZhGIZhGCbjOOXwAteufsSlw+aioYiU4oWVUauqKhp2+IjTp16iVQYFk5s2K44SJUzQupcXNi8zROM6aqLq5cWbKAyb/A1hEXIYOKQGsoqoqFicOvkchw88xpPHLpBIJKhazRYDBtVA9Zr5s2wcDMMwWQFnFDJMDsbLMwiOl99hzGDtRCehFNInnDFWHyEhUTh7+kWG7bN8BWvs3NMTtx7EolDVryhYxQVmpZzRdbAXKlUpgG07emS4NqIsnJ65oWPbLShVbC6WL7mMQtZhmDlOG60by2HvrluoV2uFKMlmGIZhGIZhMhf7vfdRs4paMiehlHo11FCjshr27b6XYftTVJTH/iP9YWVjihbdPGBe6ivyVfyK0nVd8MlFHgeP9oetrSEym5DgSMycfhr5radjcP/98HT5grED1TFukAZcv3xG65absG7NtUwfB8MwTFbCGYXMX4+fXxiOH30GD48gGBqoo3Xb0jAx1c6Uffn6huL82VcIDo5AvnwGqN+wiDB0fhcas0QCVCgtWyQ6r5UiDPQU4eoaiIykYeOicHo9A6ccnovSZ9IubNq8OIoVN0NWcOf2Z3RsuxlqKoCKshwuHbZApbLf52D6aH006OiB/n1249a9CcJx6e8fhrOnXyEwMBxW1npo1LgolJX5EccwDMMwTPZDZbmXL77F48cuUJDPg9p1C6JiJZtMCb7SvhwvvcOnj77Q0FRG46bFYGSk+UfbdHX1R6sGaVevlC+lhFNXApCRGBtr4dT5YXj08KuYu5iYOJQua4kmTYv9kX39K01bWrfcgA/vvBAZGY+Jw3Qxb4p+4jmbPFIX0xb4Yeb0MyK7sExZS8TFxeOK4zu8f+ctJH3IHuUKGIZhchv8K5r5q1mz6ioWzb8AiSQe5qZK8PSOwaz/zmDI8NqYNqMx8uTJmKRaMgpm/3cGWzffQmxsPNTV5BESGgcTUw0sW9kBDRsV+a3t6umri38/folByaKpG4h884tDQFAs9PVTR3f/FHIOduhUDllNfHw8Rg49gHIllfD8dSRG9tdJ5iQkDPTlsWqOAeq0ccP1ax+EY3H9mmvCgNRQl0dwSBwMDNSwcGmbDG32wjAMwzAM8ztVEn167IKLSyAszJQQFRWPpYsvo3wFS+zY3TNDA9jkpBo17CA8PUOgqSGP8Ih4TBp/HL37VsWsuc2hoPB7DjZ9PQ18/OKf5vsfnWOhp6uFjIacchUq2ohXVrNuzXXhJKxXQ1WUPM+Z9N1JKB3b7In6OHAiDNu23EbnrhUwfPB+uLoGibmPiIzH5Akn0KVbBSxY3JoD2AzD5Bq49Jj5a9m14y5m/3cWQ3trwuWxDT7es4KHkw3+G6uHtauuYvkSxwzb14ypp7Bpw01MG6ULrxf5EPghH546WqFsMQl6dt2J27e+dyv+FWxs9IURuXJzIOLiUjcXWbMtEPLyedC8ZUn8Ldy4/hHOzgHo01kTIaEStGysIXO9GpVVoKergJXLrmDlMkeMG6wNd6e8CHifDy+vW6NW5TwY0GcfLl96m+XHwDAMwzAMQ7i5BqB9600w0ovCg/OW+PrYGh7PbXBmnxm83L3QvvVmREYmdBD+Ux7cdxZNRUoUiseji5bCHvV6kRczx+li+9bbmDLB4be33bZDOZy+FIa3H6JTvffmfTTOXApFu45ZH2DOzMD1nt330KODJj45x6B5Q3XIy6fO/qRlLRqq4d6dT+jUbgvyWcbg3rmEufd9nReLp+vj0IFHGD38ULYcB8MwzO/AjkLmr4Qyy5Yuvoju7TWx5D9DGBokRPC0teQxdbQexg7WxbrVV0VJwZ/i4R4ooojzJuuJbevrJURqKQPw+A5TlCmhhMULLvz29idPa4J7jyPRpo8nnF5FiWVePrGYOv8b5q30F9mR+v/PPPwb+PjBF0pKcihTIiGDMiIitYOUiI0FoqPjce/uZ0wdpSciukb/P89FCirhwCYT1KyiivlzzgrBaYZhGIZhmKxm6+bbgCQW5/ebolyphAoJasrRuK46Tu0xxdu3PkLqJSNYsvACihZUgsMusj8T9qWnK49JI/SEw2rnjrtw+Zp2VuCPoCoT2/wGaNjRA4dPhYhGd9HREhw6GYJGnTyQv4Ah2v9FjsLQkCh4e4WKRi0KCmnbowRlDgYFRcDGQgFn95kmSgaRvviogbpYu8AAhw89FXI+DMMwuQF2FDJ/JQ8ffIWXZyiG9ZGtCTKsjzZCw2Jw+fK7P97XyRPPoagoh4E9UpeNKCjIYUQ/bdy5/UU4FH+HmrUKiOYij17IoWx9F2jk+wTzUl+welsIRo+tB1tbA7Rqvh4Vy8xHkwarhdMyNDTBoZgb0dBQFoankb48zE0VsPdIsMz1TpwPRWhYvHACDu+b+jyTET5qgDZePPcUGj0MwzAMwzBZjcPxp+jaVh26OqlLfimoTA1CThxzyhCdbOpKPKyvlgi4pqRfN20hjXP82LPfts+OOgyCbUErdBrgBU3bT9DK/wmdB3qJZbPnthQlzpXLL0C1iotEyW1utr+UVRRFtqCnVywa1lbHsbOhCAuPT7UeLTt6JgxBQVEY3EsLKiqpf153a6sFAz0FHDv6e3PPMAyT1bBQAvNXQh3KCHI0yUK6PCQDMgr9A8JhpK+QqiuxlHzWiuLfgIDw3xYzJhHqeg0K46rjOzg7+0NHRxU1aubHoP77sHypI+rVVEe7pkp4/ykAUyc5YOe2Ozh6ctAfC1f/KuS0o9efaD82aFQEysry2GYfjFEDdDBh9jcRge/XVSux5OP+k0iMmPINtrb68PcLEpqFP5p7f//w3x4PwzAMwzDM7xIcHAULU9kyKoSFqTy+eEX88X6CAhNsHVtr2Q1H1NXywMRIAQF/YBOZmGgJZ+Gb1164e+ezWFalaj7cuP4Bndpvg42lEuwaqyEqOg5Hj97H7p13sWV7d9EQLztKh//EHiU9wcZNimLrvo84ut0E67YHovMgL+xabZzo9A0IjEPPEd6IiCQbGMj7f7szJeS4tTBTRIB/2G+Ph2EYJithRyHzV5LP1kD8e/N+BDq0TO0su3kvwSCjzsR/iqWVLty9YuDuGSvTMfnwaRQUFPLA1OzPhKqpuxt1I5YyfvRROD39imvHLVCjsmoynZgGHTwwbPB+HDo6AFkl0r1+zXWcO/sSERGxKFLECD37VEWPXpV/2pWOnLpHDj/Bq5eeUFFRFEZZ775VMHv5bSyerofenbQwZKIPlqzzR8WyKkIn5tGzKJQqbYYevapg7Kij+PA5GgXypTaMHzyNBGlOW1rqZuLRMwzDMAzDyIbKdW/eD8T4oanfI/3p2w+iUL2OUYZ0CFZSyiNsH5JeSYm3byy+ukULu/VPKVLURLyI+/e+YNrkkxg7WAcLphokBnWXzYwXTrQBfffi3qOJsMgCWywoMEJohtvvvQ9392Do6qqgTbtyGDq81k+PmxyLpJN96cIbREXFomQpc/QbWB0d2rzBxDl+WLvQECOm+MKyzBc0qJXQRPDCtXDIy8tj8/buGNJ/Hx4+jUSz+qnlgAKD4vD+UxRattfLtGNnGIbJSLj0mPkrKVDQCJWr2GDuigAEBccley8yMh7TF/mjQEEDVK2W74/31apVKaiqKGD2Mr9UWnh+/nFYuTkITZsXg55exukIBgaG4+CBR5g4TDeZk1Cqz7d4hh6uOn7A+3feyGzOn32FJg3W4PmT15g6UhsbFhuhSL5wkdnYo8sOoReZFmdOvUDJonNEeYrTQydcOH0frVtuEmLc7TuUxbhZfjhyOgzWlkpwcY/F0VOhCI3SwbZd3XHu0gihl6Onp4r/lvgjPj753NN5X7ouCHXrFfxjJ216iY6OxeGDj9HWbqMou2ndYgMO2D8SBifDMAzDMP8e3XtWwdnLYbhyK3Um34adQcJ517N35T/ej6aWClralcKabUHw+Zbc7iD7dM5yf9HxuG37MshItmy6hUK2ylg0/buTkKAS3G0rjKGkCOzaeQ+ZzbdvoWjaaA3WrXZE0zrApqVG6N9VFSeP3UeDOitEFmRaeHkGoWHdVWjfegsun3uIZw+dMH7MUdGQcOTourhxPxb9RvvA2EhRSNucuhiGWw/jMXhYHTx4OhlNmhZD+07lsXFXMNw8UjemWbQ2ANExCTqPWcWzp64YPuQgalRejLo1l2Pu7HNwd/s9GSSGYf49OKOQ+WtZtLQNWjZdhwqN3DCyvzZKFVPG24/RWL01CJ+cY3HoWC/IUbpZBhhms+fbYczII/DyjRP6h5Zmirj1IAKL1wYiJFweU2c0RUZn8FHmXvuWsktZ2jbTQI9h3rh39wsKFjJGZhEcFIHBA+zRrL4a9m80SdTEGdBdGxevhaFF9/fYvPEmhg6vneqzjx+5oF/vvaJEZcVsc5GNSYbslVsRYuz0//cfT8KJ48/g9y0MFhY6aNuhLAwNvx8zZSsuWNwag/rbC6fsyAE6yGupiPtPI7FkbSA8fYGte1siK6DMyI7ttuDhAxfUraGGJrWU8OqdlzDSdmy7LbI7tXVSR/gZhmEYhvl76dSlPE46OKF5t0/o300TrZpoICJSgn1HQ3DgeAgGDKqOMmUtM2Rfk6c1RpMG71G1mTvGD9VBrSqqcPOMxfodgXA4H4aFS1pBRychGy6joG6//Tqry7SpNdTzoGl9Vdy7/QmZzfTJJxHwLUB0ey6U/3uVybjBuqjX3gOD++/F1VtjU40zNjZOdCsOCvDHlaPmIhuT1nF1j8GIqd+weuUVHD81WOgtkrNRRVVROAZTnrMx4+vj0oXXqNbCHeOH6KBeDTV4+cZi0+4gHD4Ziukzm4qsz6xgzaqrmP3fWVEKTvMfFh6JHVuuY+umm9hj3wc1auXPknEwDJN7YUfhP4Q02y0jnGO5gaLFTHH24nAsmHceo2e8EuUddOiUYbZyQ2OULpMxRhnRvWcl4TBcvOA8GnfyEMtoX/UbFMacBS0zpMQ5KdJzmFYzX+nyzD7Xhw4+QWRkDNbMN08lnE3Cz51aaWDH1tsYMqxWqrGsWXkFBfMpwn6DiWj6Ih0vGVb7NxqjThs3fP7sKxq2/Ig27cpATU0JC+edQ4tuCXNP1KlXAFv2tEShwpnnKE3KpAnH8e6NO26dskCV8t8dglQC1KSzh4hMb97eLUvGwjAMwzA53Sb9V+xRCmruse8tNKX37LyLtdvcxfJ8+fSweFlr9OpTJcP2ZWWth9MXhmHGlJMYNvltYrWFbX59bNxil+HZhASdx5QVNUmJp/4fmXyuKZvQ4YQTFkzRS+YkJPT15LFkhp6wz+/fc0blKnmTvX/h3Gu8euWNe+csE7sVE5bmiji42QQlarti+5bb2Li160/1G89cGIbpU09i7MxXiI1NaORiY6OLlWvao2v3isgKrl97L5yEU0bqYuZ4/cQsz5Vz4tC+nxd6dtuBR05TMrTSiWGYvw92FP7l0Be3w4nn2LbpJh49chVfFrXrFMTgobVQrYYt/nYom27H7p6ikYiPdwj09NWTZaRlJK1al4Jdq5J4/coTwcGRsLbW++3mJT+jdGkLqKkp4NDJEEwfo5/q/cOnQoWzMCNKq3/Ei+fuKFNCBWYmsh8lzRqoY+8RLwQFRSSLYFM58vlzr7F4un6ikzApNSqroFABZZw59RL16hdOV7OXRk2K4t1bb3Guraz0YG6ROXMvC2/vYBw/+gyLpuklcxISFcuoYM5EPYya/hwz3QMz7ZpgGIZhmJwMfUevX3sdpxycEBYWIyRgqCyXHGXUOOJvhjSYp0xrjHET6sPlqz/kFfIIO/FPmm2kRd68Btizv48op/3yxQ+amiooWswkU/ZFVKlqi0On3mLWBGpml9ymCw6JwznHCAwckrkZbO/eeCMmJh5NZegDEhSEVlaWw3Mn91SOQrI1SxdXSeYklEJB8F4dNTB35Yt0ObfJxtu2swd8fELw+dM3qKsroVhx00ybe1lsXHdD2OazJ+onGy81Xdy7zhhW5Zyxf99DmdU+DMMwUlij8C+GvtCmTHRA/957oSLvi2Uz9TFnoi48Xb+gVYuN2L71Dv4VdHXVRGZZWFgUrl19L3Q7SLQ4o6Ev5GLFzUQHuMx0CGlpq6Jz10pYtDYQjjeTa948fx2FCbP90KBhIdjmN0RmoqQkj6CQtOcxKDjhPUXF5D8AoqPjRIanoYF8mvNopC+PiIjodI+FPlO4iImY+6x0EhKPHnwVBmoHO9ldpjvaaYrjvX/fOUvHxTAMwzA5gVs3PqJh3ZW4edUJo/prYM18A5QsGIGZ00+Jsk+qTvgXUFJSQP4CRjA00MDd21/EvJB0SWZgYqotbKLiJcwy1VE1YHANfPoShTEzviE29ntmYXh4PHqN8EFcvBx69KqEzERRKcGeDA6VbZOGR0gQG0udjFPbneHh0TAySHt+jAzlERkZl0oL+0cYGWkKh2SJkuZZ6iQkqBt1RzvZpeCGBgqoW10Vt29mfik4wzC5m787fPePQ6n0WzffxrqFhhjU87vjZMwgHYye/g2Txh9HzVr5hcHyt0OaIlMnHcfNG58Tl1HJx8QpjUXpam7kv9nN8PGDNxp2+IjqldRQtoQS3n+OwYWrYSha1Bir13cS60VExODg/kfYv+8BPD0CYWCggfYdy6Nbj4Ry6T+hQaOi2Ln9Hu49jkDlcqqpHNW7DoWgeo18IqKaFDU1RVhaags9wq5ttWR2h3vkFInRDbKmbPhP+V7qLfv9xOXptzEZhmEY5q+AnID9++xBlfJKOLHDFGpqCY4Tsk1v3I1Aky7OWLX8CiZOaYR/YS7mzDwL+z33ERqW4BxVV1dEpy4VMWNWUyGlktsoX8Eai5a2xsRxx3HsbBhaNFRDVLQEJ86FCy1GyrCTBs9v3/qEbVtu48mjrwlVTnULo9+A6okdlH+X0mUsYGiohp0HgmVmBu45Eixstbr1UlepUCLB1k1vEBYeD/X/X5tJcbwZIbJf5eVzR37NzzIfE0rFs3RIDMPkQnLHE4/5LbZtvY1K5VSTOQmlXxALp+lDX1dBOHn+hVKX5o3Xws/LHbvXGuPjfRs4HjVHiYJRGNjPHruzoBNbZqCqqogDR/phy45uUNQwx/kbCgiKNMSyVe1w7vJI4RAMCoyAXbN1mDjuGCwMAtG/ixKK2oZh7uwzaFhvpShL+RPq1S8knJJdB/vg6YvvEfHQsHjhjL77MAJDR9RJ9Tm6Bnv2qYr9x0Nx91FEKgNn6gI/Efnt0q0CcgPlK1pDQSGPEKuWBZWIUzlOxUo2WT42hmEYhslOTjk8x7dv4Vg73zDRSSiFGkf07qSJ3TvvClmSvxlqmtG9yw7s2XUHowZo4sU1K7y8bo1xgzWxf+9ddO24LdfOQe++VXH15mjUa1QW1x8o4+ELVXTpXg237k1Aw8ZFxTrLllxGq+Yb8enNe/RoK4+2TeRw+fwT1Ku1QugL/mmm5qChtUXjkHXbAxETI0m0KU9dDMWkOf5o3aYULK10U32WAudkt85Y5JdKa/H6nXAcPR2Knr2rIrdQuUo+HDoZJlM38ptfnAjSV8lkaSKGYXI/nFH4F/P8mRtG95fd2UxFJQ8a1FKF0zNX/O3MmXkGJobArVPm0NZKKDnIa6UoOsENHOeDmdNOiaxCDQ1l5DYUFOSFNiK9ZDF1sgO+fPLC/XOWKFvye4T1w+do1GvngeFDD+LwsQG/vX+Kru472Fd0+y3f0BXlSqnAUD8Pbj+IQnhEvOiuRw1dZDFgUA1cvvgG9du7oGcHTTSqowb/wDhstw/BnYcRWLaybZZ1h/tTSMCa9ClnLn2BKuVVUD5JNJscqDMWB6BZi+JZXhLNMAzDMNmN0zN3FMyvjIK2srPlmjdQx4adHvDyDJbpyPlbOHP6Ja5d+YDzB8zQoNZ3Lb0ZY/WFTVq37Wc4HHdCuw5lkRsh6Z1lK9ul2WBj4bwLmDVeD1NH6yVmvM2fYoA+o7wxZMB+lC9v/Ud20rARteDmGoARU+9i4ZpAlC6uiI/OcXj/MQq16xbAslXtZX6Orrk581ti6iQHPHkRjV6dNKGtmQdnL4dhz5EQVKtui959M67hTGZDpeCd2m3D3BX+mDqKdDAT5poyJnuN9BbNdXJLIJ5hmOyDHYV/MaQfFxKWtn4c6XjQOn8z1GTi4oU3WL/IKNFJKIWMlGmj9bDjgLOIdnfu+nd9aVIHuONHn2L+ZL1kTkKiQD4lLJymh+5DP+DjB590lZ9/dfbHoQOP4OkZLBrCtO9YVnzOwlIX126NxdkzL3HuzCtEhEej/yATEaH9kcFPGZGHjg3A2lVXsXvXXWza7SmWV6ueFweO1E1XE5OcxKKlbdCh7TdUbuoqOj6XLKqEl2+jcf5KGEqUTNt4ZhiGYZi/GbI1Q0Pj0yyJlOrKSXXm/lb27bmPahVVkzkJpdSqqoY61dVgv/d+rnUU/oitm26hVHGVZE5CabOQjUuMcPqSs6jwmTyt8U+3RZqCJ4454ekTF8jLy6NuvYKo16CwCF4vXtYG3XtWgv3eh8JpWL6yOhYuK4vqNW1/WI47YFB10Vxmzaor6DPyq1hmZqaJsRMaYejwWiJjMbdA9vOkqY0wc94F7D0ShuYNVEXG5NEz4YiKBnbu7SWqjhiGYX5E7nnqMb9MvQZFYH/sGWaN1xdfxEnx9I7FxWvhmDEzoRzgb4Wi05R5X76U7GxBKwtFGBsqCmPib+PZUzdER8ejdTPZxkCbphroDm/cv+f8Q0chGfaz/zuDdWuui45p+fMq4YtLDJYvdUTPXpWwaFkbEZ20a0Vdn2VnNv7IWTh+UkOMGV8ffn5hUFFWEI1achrUoZAcr5Q5mFaTGm0dVTicGYKjh5/igP1DHDkbBGNjAyxb1QTt2pcVx8owDMMw/xrkxFmz6houXQ8XgbSU7DoYghIlTWFsLLsh2N+Cu2sAmtRKu3qFbNUjZ/8+e5R4+MAZI/qoyXTWaajnQaM6qrh/77uOeFpQ85e+vXYjICACJYqqiCYlpHlYqLAh9h7oCxsbfdFAZMFi818eY6MmRcWLZHuiomOhr6+e43QJg4Mi8PnzN+G4JG3FtMY3dnx91KiZH9u33MYpRxcoKiqiS/cy6NW3ipgjhmGYn8GOwr8YKu08fPAxug31wsbFRtDTTYjUfnWNQceB3tDWVkWnruXxN6Onl2CQfvgSkyqrjvDzj8M3/1jo/4WRNWmpwZFTIXj9Plo4TCmS3bm1phBrjv2/DM7PjCAy7teuvo75U/QxvK+O0BeKjIzHNvtgjJ7xAFo6apgxs+kfjZXGQB3ichr37n7B3FlncP9eQnSZoPKVGTObCUM0JSoqiujavaJ4MQzDMAwDVK2WD+UrWKLvaC8c3S6PimUS7DGyJeavChCZ95u3tfphxtffgJ6+Bj588U3z/Q+fY6Bv8HeWXtOp/eoWi1HTfOEXEAcbSwX06qQFW5uEcnTqlvyz7sCfPvqia6dtoinOxsU2yGedEIC9/yQSPYZ5o0PrTbh+Z/wfB2Yp8JvTCAgIF0H7o4efICIiViyzttbB8FF10aNXZZn3DuliszY2wzC/S84KkzAZStFipti6ozvOOkbCsqwzmnZxR9227shf2Rlf3fPgwJH+0NGRrWH4t0Clr5UqW2Pl5qBEYeOkrN0eSOYLWtiVxN+GqooiFBSAiXP88OptNN59jMGg8T7IV8EZtx9EYP/xEGG4kfbKj7rzrVt9FUP7aGPicL1EEXLSuBzaRweThuti2+abIsKZ3QjBaofnaN1iA2zMp8DWahr69d6DRw+/O/l+BdLTadNyI+KjvGG/0QRPLlth+ypj+Hq4oEWTdXj29O/X92QYhmGYP4WcGDv29IKegT6qNHVFhUauaNnDA1blvmLeSn9Mmd4YrduWxt9O+47lhFP05duoVO+9+xiNUxfD0LZ9OfxtRETEQElJEdv3B8PhfCjcPGKxYVcQClb5imkLvsHPPxbnr0agRq0CP9zOpg03oa0pJzpnS52ERKWyKnDYZQpn5wCcOPYMOYEP730wdtQRFLadASvTyahfewX27LovGtr8KmRjt2q2HmdPPcGUEdp4eMESlw6bo2rZOIwbfQwL5l3IlGNgGObfRk4iqyVSDiE4OBja2tr47DIHmlqps8GY9OHrG4p9ex7g8cOvkFfIg9p1Cgr9k5zcvMPTI0ik1tMYSd/tZ1HGH3H71ie0a7UZ9WuqYs7EBL0+Kr1euy0Qi9YGYMToupg2owlyAy+eu4sSi3t3PwnDu3qNAujbvxoKFzFJth6V8VavtBhWZnGw32AiNAkJZ9cY9BnpjcfPIyEvL4eadYph+64eae7v2tX3aN96C5yuWqF44dTXi5tHDKzLOWPrzm6JZcdPn7hi7+77cHHxh66uOtq2K4P6DRO0YzILeoxNmeiArZtvo3olNdg1VkN4eDzsj4Xhw5dorFrbAZ26pD97Nj4+HpXKLkQ+yyic3WcGRcXvkVoSg67Vyh15lA1w/vLITDoihmH+JkKCI5HPajqCgoKgpZU7mjQx32F7NGMgJ8n5s69x6uRzhIZEoUBBI3TvVQm2tobIyU4usr3i4uJRrJjpH8mjkLZe43qrEOAXgKUz9dH2/9IwJ86HYtxMP6hpauOi40hoaOb8a4wy3MjWO3HsqXBk2eQzRI+eldG0efFU9t7QQftx2uEZtq8yEsdMFS8REfFYuTkQ0xb6oXB+Jbh5AQ+eThYa2GlRrOBM9GxPGtsGMt+v184dyprW2HugT7LfP/fvfhGB8eo18otKKmm1UWZB5dGU+aijJYce7TVgbCgPx1sRojkKNfgjjUCS7EkvixdcxLrVjrh/zgJFCyW3xRes8hdzeO/RBNjmz7n3EcMwuc8e5dLjfwD60h01pq7M9644vhP6FdT9mL60SNew/8DqKFjIGNnBly/fMG2SAy5dfCtKZYm8eXUxbmIjdOj0e1FWypjbs783xo06LKLYyspyiIqSQE1NQWh4jJ/UALkBEnkeN/ooLMyU0KqxGuIlEhw781hEKNdu6JRM/JqMt9DQSJzeaw1jw++3uY2lIk7sMoVlmS9QVVPHijQ6wEkJD4sW/xoZyDZojAwUEtcj59qEMcewa+d9WFsooVwpJXx+F4tunZ+JrM59B/pmWjnH2dMvhZOQmtYM7KGduHzySD0MnuCD0SMOi9InK2u9dG3v9s3PIjK9e5VFMichQWXbU0fpol1fN7x57YUiRZM7aRmGYRiGSY2CgjyatywhXrK0gOl7/NyZ58I5V7iIKXr1qYpmLYpnS0lyTEwcliy6hB1bbyMwMFIsI7uxY+cKmDGr2W8F29XUlHDEYRCGDd6PbkM+oKe8nHBgUdltzVq2WLepS65wElIgv02LDUK7uVUTdViZK+D2A3f06bkHzZoXw5Yd3RMdYa4uAThy6ClWzzNA+xbfJWZUVfMIG+3dp2gcOB4K+0P9fugklNqaRgZp25FGBnngGZBgt16+9Bb9eu1GXFwc6lZXQVwsMG/OOyxbcgm79/VGtRppV9P8CeQM7td7tyiPpsxHaSXOiP66uHA1DHY932HDuhsYMapOure5Z/dddGunkcpJSIweqIMVm4Owb+/DP5YBYhiGSQo7Cv9RpA0qSHuOupD166wmOmIdOPEY+/c9xLadPYSgb1ZCRmLzRmuhoRqDDYuNUKuKKty9YrF+RxCGDjogopcDB9f4rW1TBO+R0xRcu/oBXz5/g46OKho2KpojdUhk8dzJXTgJB/fSxorZhlBQSDCal82UYOB4bwwfchCly1gkNiU5f/YlWjRUS+YklEINSUin8NoDxZ8ePwklE443I8RnUnL5Zrj4lzIa6Vraveu+OHd9u2iJjEXi2p1wtOvrjhFDD2DXvt7IDLZtuYWqFVSTOQkJGsOKOYY4fCpMjC29maPOX/3Ev1XKyzbYpcvpmmVHIcMwDMP8mR5wl/ZboagYj86t1WGgp4xL1z3Qu8dudOxcDqvXdfijypLfsZEHD7DHmVMvMLyvNrq0MRRNAU+cDcWS9Q/w6qUHjjoMFLrEvwrpMR86OgDv33mLiheiStV8qSpDcio0N32674SGShRu37aCpfn3OTh5IRQd+r/GquVXMG5iQhD+0sU3oFPXvb3szJV+XbWx53AIdPV+LoVUqIgxHG8GYMyg1DqO0dES3LgbheatTIQjs3f3nWhQSxXbV3zXaPf5FotuQ73RvfN23H4wAaZmyW3GjMDhuBP8/CKwYZF1opNQSqM66ujaVhM7tt7CsBG10nVNR0XFwsszFJXLy07gICmg0sWV8NU5wW5lGIbJKFij8B/l3JlXwrGzfJYBHl+0wKwJ+lg2yxCfH1ihSV1V9O+zR6TsZyUL512AkkI07pw2R/9u2ihoq4Q61dRwaIuJaKJBTSX8/cP+KJJNDkPKmCSdmNziJCS2br4FSzMlrJzz3UlIULbbhkVG0NXOgx3b7iYuj4yIhp5O2mUN+rryiIpMEEP+EVTGYGOjixmL/OD7Lfn6QcFxmDLvG2xt9VG0mAk2rb+OAd21xUvqJCRqV1XD8ln6OHvmtRCizgyePXFDy0ayjUzKAKSy82dPXNK9PZ3/lxY5u8qeIyrhJnLTNcQwDMMwOQ3KwCKnTpkSCvj8wBqr5xlhxlh93Dxpjj3rjHHowGPs3nk/S8dEsisOx59j73pjLJ1pKCRrSH5l2hh9XDxkJrSPD+5/9Ef7oMqd3n2rilducRISt29+wqtX3li3yCCZk5Bo2UgD/btpYce224iOTrCfIiNioKwkB3U1uTTtUYKySH9G/YZFhcbj6Uupf58sXOMPL58Y9OxdGds234aGmhz2bzBOdBJKq2AObzGBRBInqnQyg6dPXVGssEpik5aUtGykDje34HT/xlJSkheZrNSIMi3H7Ve3OJEAwTAMk5Gwo/AfhEpElyy6IDKwRg7QTVbSoaycB1uWGUEiiYf93gdZNqbQkEg4nHgmIreG/y9nlULjo1JPGvfRw0+zZDzkkKRIssMJJ5E1lt3cv/sJbZqpJXPAJT1n5CS7e/tj4rKixS1w6XoE4uMlMo2Kc1cjULSYWbrmwcMjSERhy9R3wdzlfiJivHitP8rUcxHi28oqCnj9ygs+PmHo0UF25+IOLTVENJ5K3TMDRSV5hIWnLbdKuoLkKE4vdesXhpaWMlZtCZA5f6u2BMLCQgsVKlr/9pgZhmEY5l9n2+Y78PMLx7YVRtDUSP6zpEsbLbRppoEtm26K796sgnTtShRVQbvmqUthqXFGswYa2Lcna5yXpI1ImnfHjz7D/XtfhC2c3dmfBnoKqF1VtmOKyovJHvz08Zv4u1gJU2GD3XmYUL6dEirHVVTMg/wFfq6v9/KFu7hG2vT2RM/hXjh6OgR7jwSjUUd3zFqaYKtTyfMVxzdo31JdlDenRFtLXuhYO15+g8xASVFBaGSndb3SXEjHmR7oN1CrNmWwzT4EIaGpz/1Zx3B8/ByFNm3L/OHIGYZhksOOwn8M0hNp1mgtXr/0QrvmssV8KfpWp5qqEP/NKrx9QhAdHY8KpWVrvpDz0NpSSWidZCbU5Zd09koWmYNe3XejX6+9KF96gShT8PYOlvkZipqeO/MSG9ffFJHvX+0ATMYENR+h7adlAJKh8CMbWfgD/+/wpW68Pj7BcHGLhma+T6jX1g1HTockGi1b9gbD6WUkevap+tOxnTn1EnFxElw5ao6m9dSxcE0AWvfyxH9L/FGjsioWzzDA61fecP5/yQNl78mCdCEVFeQQE/3r3d7SQ916hWF/LFSMNSXunrGidLpeg8Lp3p66uhJGj6uPtduCMGnuN3j7JkTGXd1jMGSiDw45hGLilCaZ2qCFYRiGYf5WyCZZNP8CZs88g2KFlJJ1sU0KOQrfv/NF0C/aVn+Cm6s/KpRWSlMbsUIp5Uy3RwlyDpYvNQ+tW27CgL770LzxelStsEiU86bFyxceQuuRGt9RefPvZHhSQ8G0MvxoSn7ksiX9bOl6JBn04J6zKI+t29YN+St9wbwV/ggITLAFP3+NwZL1QWjRsiQMDH6sTxgaGiUa4cwcp4dF0wyE47FDfy/0HO6N4JA47FlrDF0dBRw59ETYmmnZo4SGeh7E/D/jMaOpW78QvrhE4/YD2Y7R3YdCUaas+S81VBk+sjaCguXQpIsHHjyNFPdOZGQ8dh4MRrch3qhVOz+qVs+XgUfBMAzDGoX/FPTF0qPzdrh88RTZXTE/+I6MjpFAUSnrnCA6Ogllox+dY1BLhv+KonNe3rHQ08+8TmXkpOvdfRdu3/qA/8bqols7Lagoy8HhQij+W/wRrZqvx/lLI5OVm550eI4pE47B2ztMRC6pi5uSUh6hO0JC2AUKGKJH7yro0asylJVT327HjjzFujXX8NzJQ/xtZaWDPv2rCS3GpBlwVarlx9EzT4VjLmnpMUHGwskL4ejQubTY3pCB+0XTkimj9IQxdOJcKDr290Kd6qrieM45hqNPv6qoV79QuhzLOtryKFdKFZuXqWLNfEMEBMVDRyuPMPxevIkS61HJg6qqAk5fDEOJIqmdvVduRYgoaumylsgMBg6pgcb1n6HfGB+snmeYmJVAXZnb9fMW4+v4i81whg6vhejoOKxYegnLNwZCT0cBfgExUFdTwqKlrX+pizLDMAzDMN/Zuf0uli6+jHKllBEZlbbribTnCPks1CjU1VXHxy9pV5N8+hoNvXRo6v0J5PAaPGC/cJQe3myJIgWU8OxlFOat9Ef3zjtEZ1+S05Hi7haIwQP24e4d58QmbDExEmhoKCI8PEbYQa3alsHgoTVhY6Ofan/kVFy66BJOn3qBmJh4qKjIo3XbMhg3oUGyRnBVqtli4fyLIgBbv2bqOaBAqrGJBlTVlNCo7kp4ewWhSxsNFC+khOdvojB/lT827ApE8wbqOHQyDPoG2pizoOVP54OC8BQMLpRfCU3qqWPkAB34+ccJm1j3/1I7SzYEicB76bJWOH3xHRZOk6Ry9lLjmLOOEahV7+c28O9Qu04BFCtmjJ7DfeCw20SUrBNRUfFYsDoAl66HYcuO1r+0TdIfP3xiIAb23YMqTV2hr6eA8Ih48ZujWYtiWLu+U7Y0/GEY5u8mU791qdPU9OnTkTdvXqiqqsLW1hZz5szJ0vIBJrmuyMOHrti1xgj1a6lh//HvWWZJ8fCKxfU7EahZu0CWjU1fXx116xXE2u3BiUZhUrbuC0JoWBzatC2daWO4euU9Ll96hwObjDFxuB7MTRWgryePPp21cfWYGdxcA7Bzx3cdQIro9uu1B1XLyeH5NSuEfraF69O8GNFPG5GRsULrsUTBcMyYehKd2m0R2YpJoUj6wH72MDcIwr4NJji63RR1qsRh7qyz6NtzN2Jjv2ff9e1fTZwXymZLOj/kJOw7xgfBIfFo0ryYaGrSubUGXt+0wn/j9DF2sC5unrTExiVGuHorAq8+KmPN+o5YuKRVolFB1wA1S6HrgwzNpFha6sI/IBZfXGISy5xNjBSEk5B4/DzBUViokLHourx0QyCcXiUsk/LNLw5jZ/qhaFFjVKmaF5lB6TKWWLexM/YfD4VFaWe07uUhSlHyVfyKL65yOHCkP7T+rzuYXmh+xoyrh+dvZmDJ8rboO6guVq7pgOdvpgtHK8MwDJM7YHs0Z0HltKtXOIrGDuOH6uLV2+jEwGNK6HudMrA0tbKuG3C7DuVw4244Hj1LnRVGlQXk4GrbIfOChRRonjnjFDrYaQqd7oplVEQAlCo5zuwzQ+1qqpg5/WTi9UtOtDYtN8DDxQOHt5oKezT4oy32rjeBinIczIwV0LODMs6ceIj6tVaIypOkOD1zQ+P6q+H0+A3mT9HDqb1mmDZKBzeuOKFx/VXJ9KXJjitZygxDJvom2oYEjeXwqRBs3ReMvv2rY8KYo4iJChX28ZZlxkLqaNsKE/F3XByw+3AouveqjrOXRogGL1I83ANFkxcaU9IqG109KiVWwNMXCddJnjxyotpI6iQk3exPX6JhYamLPv2q4e3HKCxcnTzrk8ZImtuu7tFCGzIzoESBPfv7QkFFE6XquKCmnRva9/OETfmvmLPcH5OnNUar1qV+ebvlylvh/pPJsD/UBwOH1sfkqU1x9+F47NzTK1d0ymYYJvchJ8lEK2n+/PlYvnw5du3ahWLFiuHRo0fo3bs35s2bhxEjRvz088HBwdDW1sZnlzlZaiD8rUyd5IALpx/g0wMrXLsTgfrt3DFmkA7mTTYQGYZSp06bPp548xF4+HTyLztX/oTHj1xg12w9qldSxvzJ+ihfWkVECzfvCcLMpf7o1r0Slqxom2n77997Lz69e4enly1kRuZ6j/TC3afKuPNwojA26tVcDkOdYFw4YCYMlqRMnPMN63cEwu1ZXjx/HY3GnT0wdHhdTJraKLE0pE6NFZgzUV9k/iWFRJrtengKh17SrLUD9o8wavghoQ1j11hVGFonzocLJ+GGLV3w7o03Nqy9Aten1qKzcUrqtHFHjJwRTp4dmqysZdGC8/j0MaF0mA6bHLZzF9iJCGZYWDRKFpmNNk2VsXW5UbJ5oS7ZVZq6wdjSSnTwCwqMQBu7jXj7xhPtW2gIw5bKSvYcCUUeeSUcPzU40wW7ycAk0fMnT1ygIJ9HlBu3a18GDx+6CI0fOm8VK9mIjt6/olnIMAzzJ4QERyKf1XQEBQVBS0t2908m82B7NGdBTqD6tVfh6jELVCmvgiLVnUUFxKk9ZokNMih7bNmGAEye54fN27qidSYGimVJyjRtuAYerj5YNktfaBVS5tpZxzCMm+mHyBhVON4Y/Uvlo78CBaK7dNiOp45WKFk0dZWG481wNOzgjktXR4hA6Ya11zF39hm8umGdqoSbgrflGrhgy3IjtGmqgcadPeHjr4J7jycJ+RSyi+pUXwZl+SAhM5NUJ5J+E9Swc4eJhQWOOgxKXP7V2R/tWm2Em1sgmtVXg5W5Im4/jMST55Fo1bokxk9qiGqVlmLHKmP06JD6ebfNPggDx/mI3xnWNgk28Jcv3zBtkgMuXXybKLWTL58exk5oiA7/rwgZOewQrl56hseXLGBsmLxKhxyAJI/z7OVUmJhqY8nCi1i88BLKl1ZF+xbqQrP7gEOYkN6ZPbc5Bg+rhcyEuhWfPOEkJHzCwqJE8xpqtKKgKI/jR57C3z9cBOPbdSz707JrhmGY7LBHM7X0+M6dO7Czs0OzZs3E3zY2Nti/fz8ePMi6JhlMQgTtzu3PuHnjI8LC47BxdxC6ttHEqrmGGDXdF/uOhqBxXTUhkkulo6pqyth/uF+WOgml0bJ9B/tg1LBDqNTEVeiLRERSE4o8IqNu5pzmmbp/b+8glCismGb6PpUPHD+XoFP4/p0PXrzwElHXlE5CYtQAHWHgnjgfhp4dtNC3iyb27LqLsRPqCwHj3bvuwcxEEROG6ab6bPMGGmhYWx27dtxJ5iik/y9T1hLbt93B7TufhFOvXcdS6N2vCmxtDdF53zbUrqYi00lI2DVSw9SF36PI1PFt7KijaNFIHRvmm8PSTAG3H0Zg0RpnoWN59tJwsd3Z81pi1PDD8A+IE6UeeS0Vce9JJBasDoSrhwTrtiWcFyrJdjgzBLt23MXe3fdw+JQ/9PRU0alrVVFKbWaug8yG9iF1xhKfPvmiaaO1Qt/I2kJJNINZv/aGKPHeta83ipf4eUMXhmEYJnfD9mjOgQJ6u3YkdJzdfzwYRgbyOLnbTARU81d2RuO66jDUl8fFq2Fw94rD6HH1stRJSCgpKeDg0QEYNmg/egx7h76jfUCVz1FREpQrb4EDW7tnmpOQ8PJKsDWLF5bdOVe63MszGCgDHDn8GK2aaMjUeSxVTFmUCNsfDUHvTtpYNddAlK86Xn6Hho2KiCA9dTE+t98sVTMZA315TButgx7DPgl7imxCgpx7V26MwYH9j3Di6FO8c45A3nxm2DelEuo3LCw6RhN2jWXPkV0jDQwY6wMnJzexLXI8Nmu4BppqsaICpmZlVbh7xWL9jiAMHXRA6FP2H1gd4yc2gOOl16jewh2TR+qgbnU1ePnEYtPuIOw+FIKJkxsKJyFBzsoy5ayweeMNzFr2Rdj21arb4ui8mqhZK/MrpkhuqH3HcuIlzRKdOO4Y9ux6IBqq0G+Az1+jMWfWGUyb0TTTHZcMwzC/SqY6CqtWrYrNmzfj/fv3KFiwIJycnHDr1i0R1ZVFVFSUeCWN4DJ/BmnM9eq6E/fvf4WFqSIMDfJg5FRfTJ7rJyJ9TletsHFXEB47RcHVIwZKykq4/WACjI2zJ+OhVu2CeOQ0RXTH/fTBV6TTU/aXoWHmR9uMjbXx8p2XcKzKcha+ehcFY5OE8gjSQCHy28gW3zY1VhDRccqIJEiLZd12D7i7BwptmPdvvVCzinIqvUEp9WqoYu7K1CLUhQobY9ES2dom5FAND007QTg8QgLF/2fRUTThv6kn0a+rljDKpMeb10oR9WuooXZrd1ECvWN3T3TtXhFq6kpYNO8c6rV1T9xe/gIGWLOxaTJnm4aGMoYOry1e2Q2V4rSz2wR15UjcPJmQtUDH+fRFJPqP9RXR8Gu3x8HERCtHlWN9+fxNZFLY5NWXqWvJMAzD/Bpsj2Y/ZFstWXgJy5dehqpKHhQrrIRDJ0OxeU8wenfWwpNLlqLM2OF8KL58jRFOwgWL7dBvQPVsGS9J4lDQnLT7blz/KL6fqSKBAraZjdQGf/UuWqbuMy0X6/3ffvH3C0P+2mnbC/nzKuLOw4RmMNQ00MhAEU5PXYWj8N3bBFuTnG6yqFcjYfmHdz6JjkKCMmvJeUevlMgr5Em0O7VlmFikr0co/n+9hfPOQ0UpBndOW4hyYqJAPkWUL6UME6M8mP3faSFvQ2XFp84Pw9SJJzBg7LvEzENdXRX06V8Vo8bWTbYf0nBMquOYncyYegr79z0UOtp9OmsJXXP6jUCajTOmnYaOrho6d62AnAQ5rOnaouuM7geGYf4tMlWjcNKkSejUqRMKFy4MRUVFlClTBqNGjULXrl1lrr9gwQJR2iF9WVpm/pfx3wxpe3TrtA1fPrnjrL0ZnB9b4+V1G3x+YIOGtVXRaaCnyCJcM98Iy2cbICBIgsFDa2Wbk1AKlUI0aFgEg4bWRLceFbPESUhde3X11PD8VSQWrw1IpVv0yTkaBx3C0LFzRfG3hUVCdtxDGfo1xLuP0WJurSwSDJ7QsITtKSkmOOrU1JTh7Su7wzFBXXY1NGRHkn/Uae3a7XC4uKXuVEeOp31HQ1Hn/w1MTpxwQkRkLGaM1RfOMzIgSddPzeYjbMo7i8YjVC7h5Rkk1m/dpjROXxiWTGPw44dv6NVtNzq03ZxmR+js5MD+x8LIObvfFFUrqCY6Q8uUUMH5/WaIiozGru3fNSez+16lrtkVSs9HlQpLUL3yUpQqOhvz554X5SvZRWhIJPbufoDZ/53BimWOyXSKGIZhcgtsj2Y/27bcwZJFlzBlpC7cntng+VVreDjlxbqFhthzOBjzVgZgeD8dnNhlBi0teeTNq5sj9ICpZLTfgIQmc1nhJKSuw4EBYcIGHDjeGyGh3/WqpfbcojUBKFjIAKXLWIhlFpZ6eOSU4DyUxWOnyMSS7thYIDIqHopKCfYpBYIJn2/J9yPF2zdhubp6aodlWlDmnrKyvDivsthzOEToDVaplk8EdU86OGF4X23hJPT9Fivke4yLfYZOgc/YeSAEUVFx2Lzhpvhs3rwG2HewL0aNqQc1tYRjCgiIxPYtd1Ch9AJcvJB2R+jswscnRFTbzJ6gh6F9dISTkCAd9GWzDNG2uQaWL7mUTJMxO3n4wBlt7TaiROE5qFVtOYoVnIVe3Xbiw3ufbBsT/S4jCaEFc89j3pxz4jyT855hmFzqKDx06BD27dsHe3t7PHnyRGjDLF26VPwri8mTJ4t6aenL1TW52C7za1y/9gGPH7nBfqMxGtVRT3SUWJgpYt96UxTIq4iR03wxYKw36rXzQImSlhg2MvszwbKS0NAo9Ou9BxXLLBTlsqoqcpgy3w8Wpb/A8UYYAoPisPNgMOq08YC5hS5696kiPkcd4GrUzIfF6wKFUy3ll9msZX4w0JNHiwYJEbhdh4JFFzRTs4SSiGYtSginHjkUU40pLB57j4SiaXPZYsfUeGTc6KNCz7FLh22w3/sQERExaN+BdE7U0a6flxDblkLjGzzBB+8/R2PQkJpimatLAMxNFEXDlkMnQ1C7tZsQpV420wC71xqLUnQqs+nZbadwVFEjlk7ttuLDW1dsXWGEkE+2CPtiK9Z9+9IZbVpuFE6lnMQpByc0qacmOkCnhMpp2rdUF+tkN3S9TBhzDNOnnETtyvE4f8BM6DZ1ba2M9WuuonuX7aJkJauhjosliszB2FGHcdbhLtauvITK5RdjyAD7VI15GIZhcjJsj2Yv9B22asVl9OqkhZnj9RNLXKlB2qCeOvhvrD427AzEjEXfUKqOK168jsX6zV1FY4h/CZKEIV3ooYMOQg6xePAkCkbFPmP8LF9hy91+EIGWPTxx5VYEZs5pmWjXd+lWERevheHW/YSswaScuRyGB0+j0LdLQhLAifOhCA6JS8y0q1u3kHDaUfmuLGi5gYEaKla2SfWev38Y1q25hnatNqF1iw2YM+ssXL76i+yzTp3LY9ayAJy8EJoYfKd/j58NxbxVAWLMOjpqwokWHR2PCqVV4Okdi2ot3LBlbxC6t9cSzVgmDtOFsaE81q6+KrQtibWrr4ngZe9Oavh43waxHvlx75wliheMRY8uO3Dj+gfkJC6cey2cWgO6J/wGSMmQ3tpwdg7Ai+ceyG5o7lo134BgP3dReXb7tIUoV3/36gOaNFiNt2+8snxMVJpOuvCtW27C/j03cGT/LXTtuB3VKi7Cm9dZPx6G+VfI1Lq28ePHJ0ZxiRIlSuDr168iUtuzZ89U6ysrK4sXkzFQRlgBW2XUrppaa1BRUQ79u2lj7MxvcPdWxKixDTB4aE2oqf1aFltuhgwWipA9efRFlN92aa0JVVU5XL8bgVHTfNGki4doGCJt8LFybUehwydl1tyWaNlkHaq1cMfEYTqoWEYZn5xjsHJzIC5cDceedcZivTnL/YT24/pNLRKNOtLbWbX8sjD4dq42QuVyCWWxH8iZN8EX4RFyGDA4eTmHn18o5s46JzK8yNlLTV+8fOIxcthbYYAfOT4I+w/3R6d2m2Fb6SvqVleFhrocHG9GivKPlWvao0LFBENPT1cNPn6xcHGLRt9R3mjfUgO7VpsklkJ3basluj036uSODetuwNhYE07PPPDgvCXKlfou5E7rlSupgpJ1XLDf/pHMEpTsIiwsEqUKpP2Ioy6A5CjObu7fc8aunfexYbFRMiOyZhVVNGugjkYdPwinXVaWpDhefoshA/ejSxtNzJucIC5PHbb3HAnBqOkJztX1m7tk2XgYhmH+BLZHs5cnj13g5RmKgT1kZ+TRd9900YwiEG3blcGI0XWF1Mq/BJWlkm40OfQmjzQVUjAU9F28LgDLNwaKF2FlpY3d+3qJyhspbduXwQH7h2ja5SvGDdERGWpkvx48EYIVmwOFLdGsvrpwJA6b9A216xZIlI0hu7bfwBpYsPqaCHD376YFFZU8Imi9ekugcBTOmtM8mRQKNXs55fAcE8YeQ2RkNBrWVoOKshx2bfuK9WuuY/W6Dpi70A4eHkFo3estihVWQdGCCnj1Ngav30ehcZMimDW3hdgWldwSH52jsX5noChLfnLZKlmQlzJN67f3wNBB9jhzfhiWLb6UoAU+63spNDkaHXaZom5bks45g4tXRiGnEBYaJcrtpR2aZdmjRHbbpJTROHbkYVSrqIKz+8wSG11WLqcq7EHShpwy8TiOnRycZWOiOWnfeiPySMLgeNQctaok/A57+CwKA8f7oq3dBly9NTbbq+EY5m8kUx2F4eHhqaKB8vLyOSa1+m8nPCwaRvp50mzOYWykIPQ9bt+fAE2trG1ckhOgqNn1ax9xeq8ZmtT7rr1Ru6qayOgqWNUZ/gHxYo6eO7nh4P5HGD6yduI1XaKkORzODsWMqQ7oNuRL4udJBtDWRgmHT4ZizAw/+PrFYsKkBomCxgQ5ZHfZ90antltRvYUbTAwVoKsnjzfvomBoqAb7w/2QL5+BWJcEpEkvkBy/NJYFU/UxZpBuolPvzftotOjuiR5dtosufPcfT8ahg49x+eJb+IfHond/K/TsVRmWVt8bp9i1LoWZM05j2GRfxMRKsGK2YSq9RHJUkWGwa/ttoZdHYthJnYRSChdQQstG6jh8MPsdhbGxcYiJiYeqqiLy5zfG9Ttv09ScvHo7AgUKmiO72bPzHvLnUxZ6kSmhOafGNnt23s1SR+GyRRdRvZIadq42TmzWQz8cKLhA1+DgCU8xblLDxGuUYRgmJ8P2aPZC5bQENS6Rhb5eHmGDUPO07LYjsst2WTjvHDq11sCmpd91oylIR/JAkVES7D4YjNg4wM0tCIcOPkKp0uaJjTuo+Yr9oX6YPfMMlm54iFlL/cVyeXlARZmuewkqNnbDs5eRohnLpq3JS+6nTGuEj+99MGr6K1FVk9dKCS7uMSKLceSYuhg8rGaig3DFUkfs2HYbgYERqFRWBUe328Do/7qCtP7wKb4YPuSgKNnee6C30Hck+5mkYIqV1sacJeVRo2b+xGOkjr/kuFyxyQXvP0Vh+SzDVJUg1Pxj8Qx91G/njlUrryI8PAZjB6duBkjX0JhBOmjbxx0fP/ggfwEjZBdke9I4KVvTtoChmBuSKyKHpix7lGyt7Lap6FxRZuO+tRaJTsKk54AayHQf+hmfP3/LsrEePvgYLi6BeH3TCvnzfk9mqVhGBRcOmCJ/pa/Ytf0eJkxumCXjYZh/iUx1FLZo0QLz5s2DlZUVihUrhqdPnwrh6D59+mTmbpn/U7CwMU6feibKZ3W0UxtnjjfDYW6uJRqG/IscPfwMhfMrizLblFDUr19XbRFJvXDQHDsPBGPe7HNwdw3E4uVtEtcrWcocJ04PwZcv30Q5r5aWiii7OHXyBUJDotC2oyG696wkDKakbN54C4vmn0dwcBSUFOXg5RuLkHCgV5/KmDO/JVRUEowk0gNp3ngttNRjYWYij7IlVDBhmF6ybRUpqITtK41Qp42b+JKvXaeg6BJNr7SgEmgyxkkXr7wQtpb9KGhURw07D3hBXV0J9arKbtxCFLJVxKMXochMSAfx2RM35JGXQ4WK1qJkRcrtm59EWcoVx/eIj5cgfwF91K5dGG8/Rgmh9IE9kpd7HD0dInQZt+9KKCXPTj5/9kW1Ckoyu2cTNSqpYNU2vyztSPnwoSv2bzKROaYe7TUxYbYfTp54jlFjkguHMwzD5ETYHs1eChQg5xdw5Va4qFZIyY27EYiNlaBoUVP8i9y78wUeHiEYPcBSZmBz9AAdbLcPxobFhoiJARaueYvmjV1x7vLIRB1vstOo2d206U3w/Lm72I6SkjyOHn4K5y9+sC2iinFTS4uuxKQFLuXlCw8MHWiP16+9haMtIjIebz5EimA4ORRt8xsmOjOpCuf6tfeiycijZ8DR7abJ7Ed1tTzYvNQIN+5GCk3BdZs6C5uUXj9i4uRGaNFkndBPpOxEWVB1lLKynLCLNTWoa7Bsu7WQbYIzydc3NNMcheQwvX/XGSEhkWJ+kma/kkN07aprOLj/IQIDI6GpoYQ27cvC1FQTk+f54cxeU1Fyn1STfPHaQDRqXCRRnii7+PzpG+Tl5YQDWBbVKyYkldD1lFWOwpMnnMRvkaROQil07XWw04DD8afsKGSY3OYoXLNmDaZPn44hQ4bAx8cHZmZmGDhwIGbMmJGZu2X+T+eu5bF4wQURHSSx6KTGx/0nkaK5xZjxDdPMOPzbCfAPQ15rhTSP39ZGEUHB8ShXUhkVShuheBElDJt0F117VESp0gkC0lJIXJleROkylmjZSra+ILF1821MneQgnFfjhyaUl7x+F4U5KwKwc/s91KxVAC3sSop1p0w6AQPdOBzfYYpiNV2wfHZC1+WU1KisAitzJVx1fPdTg0zKzDnNhYPtm59Pmll3/gEJ2nimZjp49vJ7x+OUPH0ZDXPzzDEaAgPDMWnccTiceI7Y2ITsD4rQdupSQZSuHD/6DKOGH0Lp4ipYMdsAWpp5cP5qOHZsvwNra10MmeiDa3ci0KmVhjCCyUlIGpCtWpdEsxbFkd1QNq+rR9qOQOpGTg7orCIkJKH0xTwNI5wyC/X1FISBzDAMkxtgezR7oW61pIk3f+VnUQJrbPj9+4UyraYs8EeBggaoWj0f/kX8A8LFv/msFdO0RwnKDhzQXQvNG6ijXEM3rF5xRQSXk0LdiKmZiJTyFax/2MivTcsNsDKHCIqTZA014qPg+JQFnpgz8wx27Okp7MMTx5xw6eI7nNlnhuWbAtCorrrMIDPZWZ1bq2OL/dt0Hz+NceacFpg2+ST8A2Vn+dK4YmIkIgORNBapySBV76Tk6csE28TMLKHpYEZCtjI15Vm57BK8vcMSl1esZI0ly9tCU1MFzRuvQWREBPp31UTJotp4+yEGW/c9RlR0Hty6H4MKjd0wtLe2OKePnkVi3Y5gxEEZc+bbIbshW5Oa5Xj5xMH0/+XQSXH1SGiul5U2aXBwBIqUkJ2JTFiYsj3KMJlFpqoEa2pqYuXKlUIHJiIiAp8+fcLcuXOhpPTv6OBlJ6TXsGhpG5EVV6uVu2jKcepiKIZP8UG9du4oXdoSg4bUwL+KpZUenr2MFoaHLB48iYSVuUJiVlX/rtpCG9B+38Pf3ic1HaFMQjL01i8yEk5ComghZdhvMBY6MvNmnxHlUJSZeO3KB0waoQsN9YRblTROZEFGnIqqXKIjLT1QRHnqf03g7BqLW/dTf8lSZt6OA6GiJKR7ryoiA+/yjQRjNin3HkcIEe3O3SrJFDB/8dwdz566/pb2CpUrtbXbhKuOL7Fkhh6+PrYRwtVTRmjjgP19dGq/BeNGHxGaPg/OW2BYXx306KAF+w0mQqvGzS0QrduWwuNXSmjT2xMtu3vgyp08mD6zKTZuzRlC6a1al8bVW+F49S71/Hzzi8P+42Gwa1Mmy8Zjbq4NFRV5maLohLNrDL66RsHW9rs2EMMwTE6G7dHsZ/7iVgiPVkLZ+m6Yu9xPNNlYuSkAZeu74uWbWKxe1+mfDVxb/V8ahkpTZUHNSAhpSa61pSL6ddXE/n0P/qjz65qVV6GiFIcrR8yE1AnZu1RiOnKArqhUOXP6FR4/chHr7t55F3VrqKFxXXXExZI9mva5Ilv1V+xRgjpcGxtrYLu97KYquw6GiOuDJIB0dFQwd4V/YpMUKeHh8ViyPgjVa+SDtU3y6htpUww6Hne3BL3HX2Xl8iuYPOEEmtaVx6OLlvB8kRcHt5ggLMgLLZuuw5CB9lCSj4LTFUssnGaALm20MHuiPp5ftYSZMWXWGsLcOi+GTvJBo47umLMiEDXrlsL5SyNkjjerId1LNTUFrN0me37Wbg+ElZVOlnT/lmJra4Rb96NSnWspN+5RVmf2lZgzzN9M9v9KZjIVKns9cKQvJIomomlFq56eOHo2FkOG1cWh4wP+qeYlKenSrQI8vWOwcVdqo4R0//YfD0Gf/3eJk0ZJSxdXguvXBO2X38Hx0ltRijBuSGptFTKAxg7SwadP/nj6xE1EeonqFVVEZM/CTAEO52SX99J433+MQrnyVr80njp1C6JkKTN0HeItHIFSAgLjMGi8Dx47RWD4yDpo2qwYatfJj1a9PEVzFmq68vlrDBat8UfjTp6iFJjEtKWQo5O60pUtORd1a65EgzqrUbLwbEyZ6PBLDkMS93710hOXDplhRH9d4agl5+qUUXo4us0Ed259gaICsHiGQaoyWdKdtGusjrevPXHnwQQ8ezkVT19MwcNnUzB0eO1kpTfZCTW2KVDQEM26euKcY5hw0JJBdPdRBBp28oCKqjL69M26EmmSImjdtgxWbQmGi1vy7sY0tqkL/KCpqSx0LhmGYRgmPdjY6OPC5ZFo0KQMFq0LFoG7iXP9Uax0YZy9NOKHmW9/O1SlYmurj9nL/BEVldzBRsHs2cv8UCAfNbH7nslVrpQygoKifjubiuy0o4efCIejLHmi9i00YG2hhMOHnoi/v3z2FfYoUbGsCi5eCxeOOVmcOB+OsuV+zR5VVJTHsBF1sHVfMJauDxAN1AjKcDvoEILJ8/3QoVM55LM1FM0Edx8KQds+nrh5LwLunrGim3Kt1u74+CVWZCcm5d7dL0LGp3zpBWhcfw1KF5+H9q03JXZRTg9Uyrx00UVMGKaLrcuNUaaEisiobNdcEzcdzKGtEY97d50xY5xuqrJoA315zJ2sJ8q7J05pjPdfZuHhs0l4+2k21qzvKDJucwLU2Gbw0NpYtDYA81b4C+kqwssnFiOn+eCQQyjGT2qUpfZzj16V8eZDFHYcCE713ulLobh+Jxw9emW/jBDD/I1kaukxkzOoV7+weAUFRiAyKgb6+upQoI4b/zhkOBCjpvvi5dso9OqkBW3NPCLKvXBNANRU5fD6fTTyVUhYr0ZlVbx6G42K1b43PvlV/P3DhE5PWuUlBfIlOG537byLqlXzJab6U3nF4J7amLnUD22aaYiIbtJyDOqUbGyigWYtSvzSeCijbt/BvujSYStqtHRDsUIq0NPNg0dOkYiLk8PKNR1EKTSx276PaICyeN0DzFyS4CxVVpYXTVqo9EXaEY+cXONGHxXdmSnTr1s7CxF5PnkhDKu23IXTM1ccdRiYqMP4IyhaTo1SShdP3X2yUR116GjnEV2XKQIuC9KfPHbGRzi4zC0yvgwlI6DGK0eOD0SfnrvQvJurKOtVVJCDl08M8tnq4ciJXomC5VnFlOlNcPvmR1Ru6o5hfbTEtU8l0Bt2BuPe40hs3NJF6CExDMMwTHqh7+Hlq9pj/qJWCAwIF9Ib/F0CoXFN2nbOzjGoaeeGcUN1UbKIsrBBl6wLwMOnkaK5XOm6LggIioOttaJoSKiklAfq6r/XnTsqKg5hYTHIn1e2LUbB17zW8rh35zOOH3sGDQ0VYQcQ1Nhs+cYAjJjmi01LjISunRTKEqUgs/2hX29KM3BIDTEPE+dcx+J1gSheWAlfvsbCxT0aTZsVFVVS0kA/2U7z55xF7dbfnX3lK1ji+KlWyeSBqHFhp3ZbRaDffqMJihRQwrOXUVi20VVkAR4/NThdTs1jR54K+32cjCYqZIM2rK2Krfti0LiObI3FJv+321+99BD65kl1tnMSEyY3EBqMs5ffwLxVATA2UICHd4zQu5y30A6dupTP0vFUq2GL7j0rYsDYB7h+JwKdW2smyght3x8irouWrRLkmhiGyVjkJGnl8uYAgoODoa2tjc8uc4TmBsNkFPQlWKb4XDSrlwdFCyoLg8fTOyFyRmLJea0U8P5TDCzN5NHBTkt0ej1yKkSU6Q4YVF18Wf4Kjx5+xdZNt4QI9Ldv4aJkgaKRKaHScMr6JMdaRKRElAA0qq2Mw1tNhchzu36eOHMpTAj71qmmJqJ8pLcXGZ0HB470R8VKNr81H1S6csXxHc6efonIyBgULmIiuuwaGaXWRCSH85MnLmJOSpexgJ5ecsfpwwfOaNpwHTYuMRLGZFJIG5McknMX2KHfgLSbrUgpXmgmBvdQxvQx+jLfL1jFGZoaefD4kmwjj6LS0xcFwM17QY4vaaJH8cMHX3Hj2gdRslOhkrXI+Myu8mgy1hfMPY/jR58iIiJBl6ZyFRuMndAg3TqYDMMkEBIciXxW0xEUFAQtrdQdzpmcDdujTGZCutXHD9/D7rVGIpPr1oPvWYIliijhxZtoKCkCnVprivLjBCmYCJiYauLh08npCrxK8fEJwa7td0UDiM+f/TCklzaWz04tJUKZjealvghblLoukza0HOLx8b610JjcczgYfUZ5iyoPct6Q3XrsbBgeO0VixOg6mP5f09+ej08ffYXMj5trgLAx23csK8pdU9pxlBX55LEr/P3DRfk22a4p369SfhGszSJxdp9Zsk6+lA1JGYhQNMAFx5E/HdOsGadx/tQ9vLsj295ctiEAE2Z/w6sb1ihcILXzm+x1ms/N27qKSpKcDtmApEvp5xcKCwsdIZNDGYfZAZ3HLZtuY8vGG/j6NaEs2sRUA737Vhel6JSNyjBMxtuj7ChkfhlK1adyUIrm1apTIJWjKDdADrt2rbbgyWUrlCqmLEo7nr6MQlSUBEULKomIbaGqX3FkmwlaN9VMLH8YPcMX63cE4erN0ShW3EzmF+u2Lbdx/MgTBAZGCM0RK2t9nD75EvnzKsOusSq27A1GzcqqOL7TNFm5LBll1Lk4Ng64e8YS566Eoe8oH3zzj8OgntqYNloPhvry2LovCIvXBsDNM1ZE4jt1qYj+g6qLsp6cwMhhh3DvppMwpmR1zW3fzxPvv2rg6q2xP91WnerLUCx/mNAclEWR6s7CoSs9j0mhDopl6rkiX+EC2LG75x8c0b9NaEgkPD2DRblxVmc2MszfAjsKczdsj+ZMqByUgmsU/KUsLVl2WW6AgqJdWytiyX8JDruPX6JFOa2JkQIK5VdC5SYuonz19F7zxM9QB+kW3TzRf1BNzJjVTGYA+MihJ9i94y7ev/eBmpoiqlbPj6uObxEZEYX2LTXw7kM0XryNgtNV60T9w6SZgWNnfsPL69ageOWEOb445xiOwgWUsXGxIaqUV8GDp5GYOt8Ptx9Egn5MVq1ui4GDawqtu5wANexr1WIjrp+wQPVKqZ1cDudDhX719dtjULTYjztur19zHQvnn4X7MxuZVSxrtgZi7ExfjOyvk3gekzJ/pT/mrQrE8zczoKubM7MJczrkMPz61V9UCVlb63F1HMNksj3KpcdMunn7xgsjhh7A0yffu99S0wPShqAOulkV0SHfNjkr/fzCYGGhi0KFjRPf+/YtwWiMiopFiZLmKF5CttEYHJQQrbU0S7gFFBXlULHMd+NfQz3BwRUS+t2PTqUVy2cZ4sS5cGzfegfLVrZLts3377xF97jwMCoR0UBeKw2cvhSI0yc9MXGYLuZO1heOs6oVVIWzrFEnd1HCQEbg0xdRWLTWH89fR+PSIXM8fh6Fdx9j0LeLpii/oPR60m0xN1GEj18soqMlGDCoBv6b3TzHaO1Jcf7yDRXLKsl0EhJkXF66nj6dx/Ydy2Pu7DN4/ykaBW2TR2ip2QY5CU1NNdGurxcObjZG2ZIqiU1AyKn77lM0lq2rnQFH9e9CmoUFNPmHMcMwDJMzoMqHaZMcRNZZTMx3nbxKlaywan2nLG225eEeiLdvvUVWX/kKVlBSSrArY2PjcOP6R3h6BMHQSBO16xRIfC8lwcFRsDT77sjKn1dJvKRQ8xIKYCelbnU1DOurja277mH8pIYieC+F9t2v1x7RjKRBLXVMHKoOD69YbNrzDAXyKuLyEWuhr0e2UtXmrqjewhVTRuoJWRv/gDhs3x8sGiGO6KcDfb08OHYmFJXLquDNuxh8cYkVlSEmRooigO7rF4syZc2xfVfPHKO1J+XLF79Eu1MW0uXOX/x+6iikLMDZM89g7bYgTB2dvPEIZSdu3hMMaxt9rNzsJ+SF+nbRFhmMFLS2PxaC2cv90atPVXYS/gFUYZM3r0F2D4Nh/hnYUcikC+oU1qr5epgaSeCw2xSNaqvDPzAO2/YFY/by2wjwD8P6zV0yfRxUGkuOow/vvyXTJJk+szkcjjth7+57iI7+bjRWrGiF1WQ05k9uNOazTfiiuf0wAi0aaqTaj7Tsg8Sjk0K6GHaN1eB451Mq52X/3ntgoBOLS5esRFkG8eRFFAraKmLeFP3EkolWTTRwYpcZJs35hqZdPBK3UbmcCvatM8akud9w52FkorOScn5JC3Dk6PoIDYuCvp66aCRhapYzs7vICHJ2TW7Qpuyaq62jku5mPHv33EO9dh6YN1kPbZtriOxPajQzdYE/KlayFkLQ3TpvR4VGrihZTEXoTFKUm+Z7wuSG+PzpG3y8Q1CzdgFoaPyelg/DMAzDMNkP2VuD+u2D4+U3mDtJD706akHr//rSU+d7w67Zely6MirTbSTqnDt5wnFcOP9GZDgRhoZqGDqiDiwtdTFj6km4u39vwGBkpI6pM5oJfb2U5Munj9sPwzCif+r9kKPp3qNING+YunqHNKtJYoUC1Ul1+Tauv4kL518Le715gwQbl+ZnzbYgbF1hLJyEBGUpXjtuIbS6R07zRXy8b8JYDeSxYKq+qGixKvtF2KE0x/4B8SK7sGfvyjA01BAB4eo186Nylbw5Ut5FV1c10e4kre+UkJwQoZMO5x1dT0OG18aMxVcRGByHoX10YGGqgOt3IzBjsT+c3eJw4nRX7Nt9H8Mm38ec5YEoUlARHz7HwN0zBk2aFRPl05TlWba8FfLlY4cXwzA5G3YUMuli9corUFKIxdWjltDTTcgcJGcYdZ81NZFHv9FPERUdh9Fj66WZxfenkJjygD77RIOK9fPMkd9GUTjiFqz2RVu7jUJkeNZ4PfTunNCU5NyVcEyZR0bjOly8Mgpm5t+bWVB5StlyFpi19JuIyqqrfc/Ki4iIx4xFfkJEmZx3soy2lLpxd259Ft3MLh8xT3QSElSO0aO9ZioDqll9dTStp4aWPTzgeDMS7ZurY80CQ+HsIkcYGXdS4WMqQR4y0Rf79t6H47XROb7sqXW7MujX65Vw1iXN0iQoer3vaCh69KmZrm3RsZLQ9MihB9F75Hv0HumdmN1p16oUlq5oK9a5cWccLpx7LQz2qKgY9CytgZvX32PB3Avft6WhhKEjamP0uHrZpvvHMAzDMMzvc/+es8iUO7DZBO1bfNdRbt1UA1UrqKBI9a9o2Ww9pv3XFE2bFc+Uahdv72DRRVdOEo51Cw3RsLYa/PzjhDTMzOlnxDp2jdVxfJulaMb25kM0Fq0JENIsZA6SBnRSuvWsgmmTHYT2IFWdJGXd9kAhNdOvq5ZMe5RIWsFB5ZnbNt9Ct3aaiU5C4s6DCFFFk9Iuow69h7aY4sCJYHQd7C3s4a+P82Lawm9YsSkQ/43Vw5DeOsL2f/cxWizfs+se7A/1FY0SczJ16hWCtrYyVm0JxOp5Rqkczqu2BMDCQguVKqdP33vajMZQV1PEutXXsHxjglYeUbiwEY6caC8cgfTqO6A6Dtg/hIdHEBoWVoebWwAuXXiDc2deJX6mXv2CWLm2I0xMWIqCYZicCTsKmZ9COidHDz/B2EGaiU7CpHRvpyU0ShwvvsTJE88xfmIDkcmVkZD+zLRJJ9CuhQb2bzRJNIqsLBRFh7HqLdwQFhGPicO/lwNQ5h4ZjcVruWLdmuupGpAsXtYWrZqtR8VGbhg5QBsliyqJDnMrNwXi7YdoXDxklsrBFxkZj2NnwtGmQ/IOW48fuwjNktpVkxt4NMy47wmOyaBtU4SWdAY/OMdi10FqlhIjhJCTlpyQoVc4vxKK1viKNnabMGRYLTRrUTzNEpbshgzzUqXNYNfDE6vnG6BVYw0oKEBEXUdN94OikjL69f95IxMpxsZaolHLp0++eHj/q3ASVq1mm6yLMemUUMdner157YVmDdegQL48OLPPTJwTD+84bNgZiEULLiIkJEqUyjMMwzAMk7s4fPAx8lkroW2z1NUgFKjt20UL67YHoF+vvaIkluyHjNbSXr3iKiLCw/DkkgUszBIqT0jjr1wpFZG5NnHONyycpo+CtglVDMULK2P32gSZnLmzzqBt+zLJbLhuPSri5IlnaNTRBYN7aaFlIw2Ehcdjz+EQUUHRopG6zAZ49J6xsXoyCR6qoHBzC4Jd4+SltGTOxsVLhINMVvaftmaCfU/7feQUidVbAzFrvL5ICJBCUjkHN5uiblt3DBlgjwGDa6Jr94o51tmlpqaE0ePqC+etqkoejBmkI64RV/cYLFgdgAPHQ7FqbYd0S/hQkJkaug0cUhNXHd8Je9I2v4FoJJh0TosUNcGsuS3E76d2rTbh2ZOvWDRND93aaYmGicfPhmLqgi+iUuvC5ZHZ1iSEYRjmR3BaDfNTIsKjERYWI7OLl7Qct5CtEpo3VMOcifpYsugSjh99lqFjuHzxLXx8wkRkM6X2nYpKHmHIkF4dOfiSQuUVvTtp4tCBR6m2SWUapy8Mg3X+fBgy0QfVmrthwFgfaOmbQy6PHDbvDRG6I1Lo/3uP8kFwaDz69K+abFvkvKJMQOpMnJQ61VVxyCFE6LikJCQ0HmcuRaBajQK4/zgCG3cFokVD9WROQim0rHkDdXz+6IEBffeheuUl+PLle/l1ToKi9wePDkDRknnRaYAX9At/hkGRL6jX1h3R8Zo4dnLwb5UEkeZQpy7l0b5juWROwpQsXngBxobA1WPmQm+Hrg/SiyFx6bmT9LFh3Q1RMsQwDMMwTO7C1ycUhfIrpqmDTM6sqGgJbp40h6uzN4YO3J+h+yfnD2WL9euimegkTMqwPtrQ1ckjnHxJEXIow3SFLXvt6odk75G+ITk0+/Svge0HIlGrlZuQprn3TAE2Nnp48jwKr95FJa5Pzj5yEpIuHn0mqdMxz/+dXtSpOCl1qqvBwysON+9976iclAMnQmBlpQNdXRVRhkwM7pXaVqN5p4Yd/v4RWLn0IsqWmAf7vQ+RU6Hg+pTpjbFuRwgsyzjDpLgz8lV0hv2xCCxc0kpmKfjPIBmbFnYlxWcrVU677Pri+de4dfMzjm43waiBuqLUW1MjD3p00MLVY2aiq/PuXfcy4CgZhmEyHnYUMj9FTV0JWlrKcHr53UhJCnXrpbIKK3NF4bBrVEcd69dey9AxuLsHClHgooVka8yVLZmw3NUjJtV7RQoqITAwEjExqXXzqAR574E+ePX+P1y7NVp0M65ZqwDMzbVx/Ewo9It8QsMObugxzBOWZZ1FI5ONW7qmEsquXacgwiPiceJ8aLLlw/rowMU9VjgiaZ6khIbFo/swb8TGyWHWnOZo0rQoPn+NQV6r1EanFHJ2mRjJ46mjFRQQho5tt4hMy5yIvr46Dh8bIDrJjZ/cBMNGNcTxkwNx8+54FC4iu4NxRhAcFCFKO4b31YaGeurHG2nKqKnmwZHDTzJtDAzDMAzDZA4mZlp4+TZGZgCWeP46CqbG8qhaQQ0r5ujj8qV3ePc2QbYkIwgNiRLNR0qXkG2PUnCyaEElYfulhJYTvj7JnYjS7DdqUEddcW/eHYtb98Zh4pTGMDHVQkCQBGXquYjKkj6jvFC6nhu6DfFCq9alMHJ0nWTbIe3AYsWMse9o8n3Ura4qKmf6jfEW9mZSp+OOA0FifcoQXLS0rXBMaqrnga6O7LJtqa16aq8perTXwKjhh0SH4ZwIOfFIFun5m+lYsqIt+g2uh1VrO4q/+/5CdcvvcPDAY5QvrYr6NdVkJgC0a66Og/tzrpOVYZh/m5xZu8jkKCjVvmPnCthmfw/D+uoIPZOkbNkbDF+/OCEoTXRtq4Eew9wREBCeYd29DAw0RKffr64xovtbSqgrLmGon/qSJgcnCUz/SKeGDCvqXNehzWZERkShTTN1GOpr48K1cDjejIChoTq69awuOpZZ2yTvdiZ1ONaukx8jpjiL8pMKpRNKRMqUUEaPDprYZh+ME+fC0LKxujiOkxfCERMrh227esDSSg9bd3ZH9UpLcPtBeJpjvP8kUhhnJYsq4/BWY5Su64Izp16KTmw5Feoi97NOchmJf0C4+PFAzmFZUCSXMgCoNIdhGIZhmNxFl64VsGPrXew+HIzenZJnvJF8y+5DISKrj2jXXBP9xvji2tX3ycpz/zR4rqIij4+fUwempbqB5IhLqQVIPPt/wN3MPO2qCsoutMmrj+5dduDalQ+oWUUNg3tq4sOXGJy+GAZXj3hUr5Ef/82vgVq1C6TKZqO/hwyvg6GDDmDxWn+MGaQrKn9o+ZL/DIQsTKGqzmhaX10047hxNxKv30eha/cK6D+wmrD5nz11w/q11/HJOVpmE5B7jyNEU5OiBZWxcYkaHj+PxtrVV1Gthi1yKjo6aujeo1KW7tPXOxhFC6b9U7tYISWcvcL2KMMwORN2FDLpYsSo2jhzygk17dwxbYyu0AX0C0joekw6JkN6ayeWJpMOCBEbm4Y4H4CnT1yxe+c9fPzgDQ0NFbSwKyUcXqqqsjPqGjQqAk1NJSzdEIA185MLElO3Oer6RgZPqWLJDRoXtxjsOhSCHr1r/PD4oqJiRedcW2vg9B4bUR5ALJFIRGkHZQTmL2Ak00koZcOWriLLr3ITV1SrqAobSwXcfxqNj5+j0Kx5MZia6eDhgy+iTLl3/wro1bsyLCx1xWepbGTazGbo23OP6ExHzU6SQsuoE/LBLSbw8IpFUHC8yK48f+5VhjoKSQSbou9UVuPjFQRDYy106lwB9RsWTreGS3ZCHaEVFPLgxeso1KuR2kkdFBwHF7doUTpDEfBrV96JYy5XwQb9BlRHteo518hlGIZhmH+d0mUs0alLOQwc9wTvPsaIILW2Vh6cvhSGuSv8Yagvj1EDEmwr0kdWkAfifmCPfvsWin17HuD61feirJjsAbLPrKxl23sUdG7dtgw273XC0D7aQp86KfbHQuDpHYfOrb83WpHaqvNW+sPMTBM1aub/4THOm30O9+9+woWD5smy0aiZSMOOHggLixKVLGnRvmNZfPjgg8nzrmDdjmDUrKwCL984XL0VLsqLO3auiFs3P+DrkwgULGaNuUuqoHpN20Sn48QpDWG/975o7Ldn3XddcMI/IE408mjWQF00Dnz4LAp1qqlgxaZ3onInI5vHkDb1ru338PSJC5SU5FG3fmHRCCajNSczC2NTbTx/7ZOmLuTzN9EwMtLA2tXXcPjAQ3z7FiYaL3bpVhGdulRI8zcRwzBMViAnoadXDiU4OBja2tr47DInx3d6/Rdw+eqP8WOO4orj+8RlpMMyqr+OKDmWGhI9hnnh9mMFPHg6OdUXI11us2acEc1FrC2UUL2Ssmg0ce12OPLZ6uPI8YGJzrOUbFx3A9OnnhKaKWMH6wpH3PPX0Zi9zA8O58OEUDMt790pwWg86xiGOcsCAXk1nHccKbIG0+Lo4acY1N9eNBKRpcXYupcnPrmp4+qtsWlqkRBUCkxZfseOPEVgQBisbAzQvWcl0VHtR58jyEDt2XUnrl55i6G9tdHBLsHIJI3DdTuCUK2iCtRU5XDOMRzx/7d5qZvbslXtRQfgPyUiIga9uu0U55dEs0sUUcSrd7F47BSBWrXzY9e+3qLxSk6nf+89cHr8Fo8uWkBHO7nBOmupH+Ys9xfOWlNjRXS0U4eSIkRJOUXUJ09rjDHj6mXb2BmG+TsJCY5EPqvpCAoKgpZWzhT+Z9KG7dGcRWxsnGhORt19Q0ISKkrIxGreUB3rFhjB3DQhD+LitTA06eyB0+eHCC25lNy5/RndO29HdHQMGtVWFRI3F69FIDxCgjXrO4mmI2k5sBrVXYV8VnJYMFVPBCYDg+KxzT5IONdi44BaVVQxaYQeShVVwpsPMViyPgDnHMNEBUlLu+QN8ZISGhqFkoVnY3hfdcyZZJDq/aOnQ9Chv5eQy6Fqlh/x3Mkdu3bcxft33lBXV0bzliXRpl1pUeb8M8guHjzAXmgbjuinLapl7j6KFIH5gMBYtG+picOnQuEfkGCQUiy5V79q+G9WswxxcFEywfgxx6CnI48GtVRFk5XzV8NFcsH+w/1QtpwVcjoXL7xB147bcWqvGZrWS+7cfPM+GqXrfRWZjiEhkWjbTB0FbRXx9EU0Tl0MRekyFkLCR0ubG50wDJM99ig7Cplf5u1bbzRtsBp5reRw8aBZsnJfh/OhaN/PCzNnN8egoTVTfXbv7gcYPeIwlv5ngBH9dYTDhnj9LgotuntBU1cfjtdHy3Sq0aW6acMtLJx3TjRXoUgxNQ8xM5HH8lmGePk2Gmu2BYpsO4I20aBhYSxZ3lZE6H7E2FFH8PSBE545Wsp83/5YMLoP9cZH59l/1J2MdHJ2bLuLJ4+doSCfB7XrFkaPXpVgYqqd6GhcvPASdu+4g4CABMFpPd08aN9CA8fOhIkuyeOH6qJ6RVWhx7hmWxDOXg7D8lXthEPyTxg36ggOHXyIw1tM0CSJQXPpehja9vGCXZuyQtclp/Pxgw+aNFgDcxMJ/huri9pV1eDpE4v1O4KwaXeQuOY6t9bA1uXGUFSUS7y2KBNh5hJ/oaVY/SfRfoZhmF+BHYW5G7ZHcyZhYdEY0Gcvbl5/B/uNxqJbsBQvn1jUb+cBBVU9mXalr28oKpdbiLIl5HFwk0liJQk5pIZO8hWZgRevjETJUuZpOuGGD96P16+9IU9Zi3GUbQj07aKNZg3UMGmOH169+95gz9ZWDzNmtUDT5sV/eEz37n5BiybrhR41Sc2khBrnaeX/hJlzWqL/wOr4k2fSgf2PcPrkc4SFRaJAARP06F0ZVarmS1znwrnXWDT/HF688BJ/U7lxk3pqiI4Grt0Jx/C+OujUSlPYVeTAXLk5COUr5hWNWf4ks5DmoGXT9SIxYMkMA6H7SHj7xqJtXy+8/ww8eDI5xzvRqGKlS4dtuHP7I6aM0EXXtpoJXY/PhWL20gBERMpBWyuh+R7pkEt57BQpMkcbNyuDNetzvt3NMEzugR2FTKZz985ndGm/FaoqEqFJaGwoj8s3I+F4IwzNWxbHlu3doED1HkmgS61apcUoUSASh7em1q27ejsc9du546jDANFQJC2ePHZFo3qr0a+rFlo0Ukej2uqJDh8y8NZsDcDUBf7YuacHmrUoka7jyQpHoTQ6So7VJvVUERkVj9MXI0RPod32fVCjVv5k2X3LFl/C6pVXsX+TCY6eDsWDJ5F4cN4q0ZiVzunAcT444BCBF29m/PZ94u8fhpJF5mDGGB0RAU/J8o0BmDLfH89eTYORUfJympzI61eeIvv1wf2vicv09VVRpKgZXjg5w+2pDdTUkpdS01ySQLhVvnwie5JhGCajYEdh7obt0ZxLaEgk2rfZjKdP3GDXWB0VyygLjUD7Y2HQ0FLF8ZODhXRMSlYsc8TyJRfh8tgG+nryqXQGC1ZxQeUaJbB2Q6c09012AzkbNVXDMHKAjpDlMTZUSHzv3uNItOjuiarVi2D77u5C/+9n3L/3Bc0bZ66jkDIi29ltgpdXsHD8mRjK4+qdKCGVQzqF8xbaJTpW6TgePfyKVs03oFk9VXTvoIU2vT1xdLspWjVJXqlz/U446rZ1x9oNHdGxc3n8Lr2674Lz+w94dsUyVXdrd89Y5K3gjDnz/8xRmlVERsZgxtRT2L/vASIjE5oq0jHVrJVfdL8mG79Dy9R2NWVuTl/kL5rbUINAhmGYrLZHc77oGJMjoYij480xsGtbCQdPxWHRujCERulj/aZO2LqjeyonIeHhHoQP77+JiJosaldVhbmpovji/BFly1miQgUrof9Xs7JqopOQoP87eTECRYsa/zRqm5Sq1Wzx4nWkKAWQxaGTYShe3ARa2r/3A+HhA2eMG30Ug3pqwfmRNbatMMa+9aZweWKNKuWV0KPrDhHdlkJlG5OmNkLT5sXQeaCXcBSOGqCTzEkojldODjPG6iEyMhYnTjjhd7l35wuiouLSPDe0PCYmXjiIcwPUQOXMhWGic+D2Xd1x6Fg/PHs1XZQrNa6rmspJKJ3Lts3UxLliGIZhGCbno6GpgmMnB2PuAjt8cNHA/NWhuHgzDwYMro3L10bLdBISpFHcpK5qKichQc0/OrVSx41r7364b7IbBg2thRdvomBjoZDoJJS+R9rSAYFxGDuhfrqchASVE2tqKAnZGVk4XAgVTfGqVvue+fcrkMwNlVtrqETi3R1rnNhpho1LjPHmpiXWLjDElk23RfVP0uOoUNEGazd2xsmL4eg1whtliiunchIStaqqCU3FfXvu40+4fuWdqPxI6SQkqKy8TjVVoSmZG6DmNIuXtREOv737e4skhqcvpqBlq1Ki8qlVY9mySG2aURPHeDx3csvyMTMMwxDczIT5bfLlM8CCxa3EK73GCaGiLFurj4wRFeU8iCNxl5+weHlb2DVbjwqN3IR2SokiykJjbu22YLh4xOPIifY/1QRMSvOWJWA6QxM9R3jjzF5TGBp8jwhv3B2EUxdCsWptU/G34+WEZh/enkEwMtESHaHr1S/0w2YfmzfcRP58ylg11zCZ4UMi2PYbjGFZ1hl7d9/H6LHf9fHI2bptZw8sXnARy5c6ooKMDnoEdfE1M1EUGpK/y8/OjXT5jwTBcyKFi5iIlxQqj4mR3ahQQMa3LMOUYRiGYZicCQVX+w2oJl7pJT4uXticaaGiIvfDpnxSunavKMp3m3b9gn5dNUX5c3hEPPYcDhFB3uEja6NESdnly7LQ0FBGt56VsWLzLdSsooqGtb9nk739EI0xM/xQvUY+4VD8/Pkbdu+4hyePv4qmePXqF0anrhWgq5u6mZsUsmEpaH/njKXQHZRCts/gXjq4cisCG9dfQ7ceFZPZ0a3blIaFhQ66dtyGSuVSZzpKoYzOXUd+3x4l4uIladqj0nMTkcvsUTonjZoUTTbfVNMXEysR2piy7FHpegzDMNkBZxQyWYaZuTZMTDWEjqEsnr+OwifnKJSrYP3TbRUvYYazF4ejcImCGD3jG+q0ccPwKd9gnT8fTp0bivLp2EZSlJUVsPdAXzi7ySFvxa/oPtQLY2b4omRtVwyb5CuMz1ZtSgmtkc7tt8H5w3vkt/CHy8cPQqi4U7stCA+XnY1I3Lr5EZ1bqcv8wtfTlRci2rdupM6kJOdjrz5VxP9/cZHt4QoJjYfPtxjo/oF2YpmyCeUdx8+FyXz/2NmEc1a2fM4Xj/4RNWoVxLkr4SLCn5K4OAkOOoShZq1C2TI2hmEYhmGyhrLlrXHhWgQiIlI7nCgofOxsOMqV/7ktSQ66fQf7YsiwOjjgEIOGHdzRqqcnnN4pY/nqdpg+s+kvj23K9MaoWi2/aMRS084NY//zRatenihR+yvUtXSwYXMX7Nl1H1XKL8b+vbdhZfQNmkqemDPrjCiFfvrENc1t37rxEXmtlIRDTxbUrfn9u2/w9k6d0UiZhcJB6Zx2xPWTcwzU/rDxXbnyVmnao4FBcbhyM+KX7fycRvUa+UVG4YETsjNH9x8PgYa6IsqWzd12N8MwuRd2FDJZBmXI9epTDTsOhOD8leQGQFBwHAZP9IWZmRaaNC2Wru0VKmyMHbt74u2nmbjzYDzefPwPe/b3+aXIbVJIsPrmvQkYNbYhXn3WxLnrCshftCCOOQzE/EV2mDnttBAkpu5ljy9aYNcaEzy+ZImz9mZ4cO8zpk5ySHPb8fES0REuLajMhdaRhamZNkxMNLByU6DQzUkJNeigyKOh8e/rXlGnaepsPPa/byhb/yuqt3DF1Pnf4OIWgw+fozFjUQAaNCwEGxt95GYoQi4vL4/Og7yFsSklMpLEy33g7BqDAYNzvuYNwzAMwzC/T6++VYTtOWKarwgUJnUSLl4bIORo+g6onu7y0snTGsPp9XTcezwBj50m486Diejeo9IvVbck3d6+g32wdWc3KGla4MxVefgE6mHhkta44DgSzs7+Qlt7QHctuDy2hv0GUzjsMoPzIxsUzAd06bBV6FClaY8qyKU5LmoUKOYhDZu0VBlLXLoRjpdvo1K999U1RgSWVVT+zFHYvWdl3H4QgaI1nFGpsQu6DPbElVvhohSXfivExcsJey43Y22jJ+SFJs31x73HpFf+/fo7fSkUS9YHomuPyqyJyjBMtsGlx0yWQiUYTx59RbOub0U5RZ1qKvDwisO+Y6GIi5fH4eO9frlTmo6OmnhlBIaGGhgzrp54pWz2Yb/vAWaM0UXTJB2BiUZ11DFzvB6mLXyEKdObiG3IisIeOfMFU0frpTLOKCPwwtUIDBicV+aYyGgICoyAr08c2vTxxLzJ+qLU2j8gDht3BWHmUj/oasvjxrUPaN+h7G8dt8MJJ9y6+QkqykDxwsqIjwfWbQ/E0g0BQvnRJq8eVqzpgNyOsbEWdu/rgx5dtsOqrLPoTKisJIdzjhEICIrDitXtUbqM7IY2DMMwDMP8HeTNa4CVazpg5LBDuHY7UmgSUiny0bNhcHoZiXET6qN2nYK/tE3KLrS1Ncyw4Lpdq1LilZKN62+gSAFlrJmfXM7GxEgBBzcZI1/Frzh44LHMUuwKlayxacNNUcUjq1kKlUtbW+vA2ES2ZnVwUIQoC27S2R0r5hjCrhFpCQLnr4Zj1DRfaGnkwds3XkLS5keSPGnh6RGEpYsuimw7C1MFUR5NTsMG7d2hrpYHUdESbN7WDSam2sjt0PXXqd1WVGvuihqV1VAwnwKevozGk+eRaNio8G9lozIMw2QUnFHIZClkRO3a1wur13WAf5ieaIJy/EI8OnWtiqs3x4oS2JxIepp9UKTzzu1PMt/vP6i6iE7PXeEvHH9SKBNw4HgfxMQCPXpVTjP6GxEZh8G9tYXxULquC/QKfYJJic+YvdwPw/vqoFwppTSjxz/j/TtvDO5vj3bN1eHhlBe715pg73oTuDnlQ/MG6sKxuWV7d+Fk+xug7tJ3H0/C0BH14OKtjzfOOmjXqQpu3x+PLt0qZPfwGIZhGIbJAjp1KY8LjsNRtmIxbN0fjZVbw2FsbiMaoE2c0gg5FWr20aWN7GYfpFtdu5qqaNYii6bNisPcXAv9x/qKgHNSjpwOgf2xEPTtXz3N5ithYdEoVUwFhfMroWN/L+gU/ASdAp/QsrsHjAzkRUCcmt9Rk71fhezjfr12IzI8CM+vWuHiIQtsXmaMlzessWOVMSIi49G3fzW0sCuJvwFKcjh5dgg2b+8KFS0rPHmrCVPr/CKbdM/+3kIWiWEYJrvgJxCT5VCUtHPXCuKVW/hZsw9VlR83+6hVu6AoS5k59zwOngiDXWM1RERKRDflb/5x2LS1K8wtdGR+liKyVlY6CA2Lx5eHeXHmchg+fomBlmYetGqiLqK31uW+omPX3ysL3rrlNvR05LF9pXEyQWUN9TzYs9YEVuW+4sihpyhe4vdKunMiJiZaGD+poXgxDMMwDPNvQlUEG7Z0QW7iZ80+VH/Q7IOqdnbt640ObTaLzMOOduoiE5GamNx5GPE/9s4DrOmzi+InYYe990YERBERceDeq87aqlVrW61tbW3t3nuPr61daq11tHXWvfcGBBFB9t57r5Dx/577UnZQUNzvr08eauY/CZqbc+89B9Om+2DxM4OvOYl57HAkMsKdmB/hibPVbAslsL8O/HtrY+mb+bC01IVE0hSU0lHCQtMREpLOLH68ujdNO1LDev4sAzZZuHtnBD74eBL7LnG/DFBQUAydOBwO526CC4UcTgcLSVqD2HmwCosea7vu0GC67OvX/kQkrTP3H+CMNavPYsO/aVBXF2PMBD8sejqQ+S1eC0rA++7rw2x6cOr4lqvNn31fjMJi+Q37tZw5FY+ZkyUqU9ckEhIjJTh9ijrTk27o/jkcDofD4XA4XUMfP3vsPJiHl5YYt7mMfBcp7OP5F9sP+/DpbYdT517GH2suYM+uy6iqlMK9uy3WrBuASZO9250mJGjz4n/fHsMXPxbj4zfMWqwvX42TYv2WCjz97PAb8mY8fSoRxkbqGDdctZ3Q7On6+P2vLCQmFMDD0wp3krSqSOTWFqO8rq1XI4fD4dytVFd2fNqbC4UcTgewdzDG+Ale+ODrBAwdoAN31yaj5qRUCvsoZmEf1Gm9FgMHubBTZyExcf/eSAyfnoWlTxhg4mhdlJYpsXZTObbtqcTLr46CWzeLG3puSoUSGurtF3Qa6u2bWnM4HA6Hw+Fwbh9PLR6MhfPX45e1pXh2YdM2ikwm4Nk3Ohb2QR5/b70zjp06g4OjCduQ+fSjA4iOl+HJuQYwMRLj0Ilq/Ph7GZyczfHc80NvuB4lW8P2NMaGWrW98L/bLRAeyXNBUqLvHTsWDofD6SzyGgpPOtGh63KhkMPpIF//byamTf4VvUdm4OHJuvD20MTVuDps3VMFWztjfPfjrQv70NPTwr+7luCzTw5ixZqL+OwHChkBXF1N8N2PM/HYvBtPf/MPcMGug5H46j0BamotqzMqOncfqsH4yR1LouZwOBwOh8Ph3DomTvbG088E4vm3zmLDtko8NEbC7Gn+/rcK2XlyrFwz95aGfby4fASsrQ3ww/+OYfJj2ew8XV0NPPyIP95+bzwMDHVu6H779XfCl5/LcTa4FoP7t72Pf/dVwsREBy6u127K3yqCi04xgfBUkTsi0i2gFmoIy7LOezFyOBzOnUJW1/F/s0RC82SFu4zy8nIYGhoiOf1jHg/PuSugtLf164Kx6a8Q5OaWw9JSH4/M6YcFj/eHodGNFUadhYykU1MKmckxFUvXWhHpCJfDMzB6+I9YvsQIX75L9/ef36JCwLJ3CrByfTn+PjIJek7CPbtiYaBZvxpjpW3SqdtR15i4V583h8O5e1Y9nvI/gbKyMhgY3B/BUA8SvB7l3G3Q17fDB2Ow5vezuHwpA5qaahgxyhOLlwyGd0+b23YMyUmFqK2VwdHRBHr62jd9f0MGfAM1oQyHt9gw78QGjp2pxuR5OVjy3DC889543CmhcHfuQyjP10PJlUr0yBTD1/3OrkBzOBxOZ6iprcbid2d1qB7lE4WcO0pqahFW/nKGeaRUVtbBxcUU8x4fgLnz+jGD37sN6pIufWEYO90pdHU10cPbpkv9Fz/7cgreen0Xdh+uwcyJEtBWx9bdVUjNqMPST3uhxqISv4SPAkrbD0wRlEoUX4lC4aVwKKRSSKytYDV4ELRNbyxkpSMoZTIUhkegOjsbYk1NmPX2gcTGuuWVjIqgr6MFL8v6rretXse67FmVZSiv1UdmmTMqaqTXfO4cDofTVaseHA7n9iOVyrFxfTA2rgtCcnIhDAy0MWWaL55+ZjCzn7nbIA/AseO92OlOHoOrm3mX3t+adfMxfcpvcB+QhllT9OBop45zIVIcOVWF4SO74dXXR3fovhIT8vHXhhCkphbDyEgH02f4InCI6w15J3aUlMxERMSGQqGQw9muG3w8+kJN7f4IXeFwOA8efKKQc8egdDNKXdPSUGDew3qwtVLH6aBa7DlcicDBrvhr85PQ1u58ahrnxgi9mIbVv51F0IUkKCGHZz8TjJxriyRzX0Rc9oROOmBZplq8ra0px/mDv6C0OBN6IkNoClooF5VALsjRK2A63LyHd/nx5mXGIPTkOkilldASSyAXZFAIMtg4+qDv0PlQ12gy2M4zlKPGAdCzq2Kioa2RAfpbOUAuk+PSoXM49+9RVBSWwtTOAuaj+kDsYY9KqQyVmbrXfN6ce5eSgjQU5CSQxA0zKzcYmzvd0i8QnAcbWV0N9mx4lU8U3qPwevT+prq6DrMf/h3BQamYMk4Xgf20kZ4lx8ZtlZAr1bFt59MsAIRzeygoqMSfa85j57/hKC+vgZOTGebO748ZD/uy1OZrQV9rP/34IH747ji01bSgqzSCVK0alfIKDBrkig3/PH7Df4fbmygsryzDivWfIzYlCppiLahBDTXKapgYmuGF+W/C1aF7h+4/LSsZxy7sR2pmErQ0tdDHuz+G+I+Crk7LEEPO/UVZZSlCI8+jqroS5qaW8OsxAJoaTV74HM6dmijkQiHnjiCTKeDf+zM42Miw/y9rGOg3ffCfOl+NCXNz8OzS4cwwmXNt6urk2LwprL4LnlQIQ0NtPDTNF4sWD4K1jeENmzWfrXDFkSRdmGbYQzdXCZ2sKpUrFvRPyEcrXkFmVjq8lQEwEtV7x5BImIyrSEcCls1/C317DkRXkZyRgI9+ehVGghnchV7QFRlAKSiQi0zEiy6jh7sPXn7y/UbhJzw+FzW2uqiyEqPOUoF+TvaIyAvDgZd+QH5MCozE5pAodVGhVoYKRQm8BwTCZ84SGBSptfu8O0JhST7Co0NQJ6uDg7UTenTrfdOr4pybo7i0ECs2fIHE9Fioi+obESQyu9h1w9J5b8Dc5NoJ5BzOrS7MOHcfvB69v/no/X34fdVpHNpkg0H9mmxkSssUGDc7B/nFWgi+9CbUKGmDc00uhqRi1a9ncO5sIugbZkB/Zyx6ejAGDXa9LY+/ds15vPbyDrjCG47oBrFIjdWpRchFtFoIRo3rjnV/LegyobCXqzk++HE5cnKz0F3pCzNYQywSo1woYfVorUY1Pl2+Aham164jdx3bjG0HN0BHrAsjpRnkkKFIlAtdHX288fQncLBxxs1QJ5Pi0tVgFJUWQF/XAH7eA7gAeYdRKpXYenA9Dpzayf5fQ6yJOmUtdLX1sGD6Mxjge2OhQBzOteCrx5y7nkMHopGVVY496xxaiITE0IESPDVXH+v/vICXXxt1V64g3y3U1Mjw2KNrcOZ0EktCfnicLjKy5Vj/x2n8vSEI23ct6dI1ZVXEp1xFYkYcfBHYKBIS6iJ1dBN6oVJUjj3Ht3WpULj72GboQBc+wgBWBBL00waOUBPUEBEXhOSM+Gt2ccO+2I6S+Gz0xXAYCaaACBAUAgqRg8igC1DqGmFo4NwbLsj+2PYTzoefhEgQsWMjMcrc2BLPzn0Vbo4e6Mp/8KkI1tGW8Im461ArrcFnv76J8tJy+GAgzIT6NXX6AhGfHcEu+2T5j7x45nA4nAcE8tfbsC4IzywwaCESEkaGaljxmRn6j8/AsSOxGDPuzq353gs0iHTublpYMk8XtHW7dU8Spk6+ivc+nIDnl3X9dklzFAolfvzuJKxEDnBGU51FtREJeG4KH+zfF4qkxIIuW5kOjw5GanZSfS0parKoMRAZo7cQiAvywzh4ZifmT13S7n2ERl1gIqEzPOGs9GRCI1Er1OBK7Xl8/fv7+O6tNdBQv7Etq7Nhx7Fh50pU11YxMUqulOHP7b9gyuhH8dCIWV1WO8rkMlZnSbTpvecr19eDRMK9J7bBBV6whxs0BE1UowJJtdH45e9voK2lA1+vGw+r5HBuFt4a49wRwsMz4GinCZ8eTeuhzXlorB4KC6uRkVGf7stRzTdfHUFIcAqObbPFrnU2eG2pCVZ8ZoGEC46wtxbwxIJ1rEvVEUpKqnH8WBw7lZd2PLzjUnQI64CaoO0kFhUfNoIjkjPjUVFVhq6ARDia0rNROjWKhM2xgC07nuCIM+3eR1FmNqJOnYWbwrtFYUfHay6ygYPQDfGnj6JOSr5inYNEu582foWg8DNwF3wwBA9hqPAQKyJlpQp8sfJtZOWld/p+Wz/GubATePu7F1hX6On3HsEb3zyLk8GH2GWc9ovl/OJc9FYGsveZ3m/2BUJkDV/lYBSXFeJ0yJE7fZgcDofDuU2kpRajtLSW1Z2q8O+tDWtLDVwKy7jtx3YvEX01B6+/sgPPP2mEq6fs8cGrpnh3uSkijtvh7RdN8NH7+9m0YUcFv+CgFBw+GI34uLwOH0NcbB4ys0pgIzipvNwS9lAXq7H77SqCIs7AUGzSopZsgLYWrJQOOH/p1DXv48DJHTAWmTPBqEEkJLRFOuih7IfSimKERJy9oeMLuXIWKzd9B4NaUwzEWFaPBmICbBTOTJzcc2IrbpaM3FT8/NdXWPT2TDz7wRwsee9RbNy1iq3UclRD6+o0SUjvuYvICxqi+lVjiUgf3ugHE5E5th5Yz2t6zh2Fj2px7gjqamLU1ApQKoXGlN3mVNfUi1sa6rwjdc0u+J8X8OzjBmwKszmmJmr46XMzBE7OxInj8Rg5yuOaKcrvvbUbWzaHorZWwc7T0hbDb4ov9GdNv+5xVNVUksSGcpRAXzBqUeQQ6qjvgMpkMnQFtdJaKAUltNHyOTdAwo8WdFBVU9XufSRcvERqG6xgr/JyazggtS4W+VlJMNTq3LoHTTJSh9kbAbASNd2/EUxZdzlYcQR7jm/Fktkvd+p+lUoFImLDkJadjKsJlxGbHMUErh7whwgi5BdkYc22FUhKj8cTM5fy6UIVnA87yV4zXei3uUxHpAtz2ODcpZMYP3TaHTk+DofD4dxe1NXFLerO1igUAqR1AjQ0+GzFtfjzjwtMUP36fbMWdT3VIh+8aoLNu6uwZtU5+PdTLeI1sPmfUHz52UFkZDQ1lwMCHPHpl1Ov6xNJXpNEDSpRJxhCU9RyGEEMMdRE6qiVym/wWap4zJoqaCq12VaKKrShg1pp9TWn8OLTouGBPirrNl2RPgxFJohKuIxBfp2byCSRacu+dWyaktWK/92/lkgH3dCLXb776GaMHjiJbaV0huz8DIRHX0RuQRZrwpI3ubPSCxLooayuGCfOH0JYVBDeW/o1jA15GKCqKVKq6+3QdiWf3id7wQ0RueeRU5AJGwvV31U4nFvNLf/Uy8rKwmOPPQZTU1Po6OigZ8+eCA0NvdUPy7nLGTGqO/ILZTh8UvWH54atFXDrZnpXJs3dLaQkF6GkpBZTxqnugvf304aluQbCLqZf099w9sOr8e+2ULz7khHiLziy07svGiNibzjCPvgdSoW83W7Yb/98g7NhR1GjrMRFHEeQ2j6kCfEtOmCFyIW+xBCG+l3zXtJaKPl3lKJQ5eUyoQ4VQimszGyumdBMiNr5J7Dh/IbrdQZaN6aJRku0LWhpHdtG6cymHRWKelG2IySkxuClT5/Ed2s/wu7DW5hISEVebwyCtciRrdn0wgB4oS9OhhxiqXuctlRWl0NbaL8Ypsuqqipu6zFxOJzbA69HOapwdjGFk5MxqztVse9oFYpL5BhxjYYrBwgNScHEUTrQ0GgrdpFwOHWcBGGhqdddXV76zGb091Xg7B47ZIQ7Y8tqK9RW5WHqxF8QFZmt8nZUc6789QwWzvuT/TkGl3BWtBdXEYw6obbxeuUohlQhRc9etugqrMxtUSEuZQ1sVZSKimBhUm9zovrYlY0iZntQTdpwvc6mMOcV58AB3VSKkHS+VCbF5ZiLnbK7+d/aj/H6189g6771OBVyBLoKfQQoR8FJ1B0WIlt0E/WEv3IEKssr8dfu3zt93A8CVdUVbA28tZjdANkrEZXVNIzB4dyHQmFJSQkGDRoEDQ0NHDhwANHR0fj2229hbMzFnwedfgFO8Pe3x+JXChAe2fQhLpcL+PbXEmzfW4lnnx/Op6KugZpa/WtTK1U9lk4aV51MgNp/3XJV7NoRgQvnU7HvL2u88YIJXJ002enNZSbYt9EGhZEpSA0LanM7Sub67LdXEZd6Dl+8Y4LIkw6sqHv0ES0k4Ao7EaVCIXJEaRg5cHyX+ZXQ/QwNGMPut0qoaFMspiAGApQY3HdUu/fh5OPNfhZAddGZjyyIxWows75251sV9NrQtGN7v7v04S9XyCGta/q9v17X9stV74Ceaj+MhKlgBR3osQKvNdZwZCswR8/v6/RxPwhYmFmjXNy+nQElddN1OBzO/QWvRzntQQFjzywdhn92VOCHVSWsDm0g9HItnnmtEAMGOsG3D5/quRZiNXG79ShRK1Vesx6trKjFR+/txeJ5BvjnNysM6KsDGyt1zJikjzO7bOFoJ8bHH+xrN4zmnTd3Y/xwMc7stkPUKUd8/b4p6oxyEK52gjWQySc6Ue0K7O2MMXyEO7qKYf3GoFZZjQwktLmsTChCAbIwYuD4dm+vqaEFeysnFIhU16O1QjXKlEVwc/S8ITGquejUGm2RhAmU1ETtCFRj/+/PTxAZE84mFGlFluptd/iwSc3WWxoOym64GHmOryCrgMJt6pTSNt9jGihDMdsWMjPuGi9NDueuWz3+8ssvYW9vj7Vr1zae5+x8c6lNnPsDElH+2PA4Zk1bib5jMjDAXwd21mo4f1GKrBwZnl82DI/N69fhDy5K+y0trYGdvREsLR+MREkyYra3N8Rf28sxakjbKakDx6tQUirHsOHtF0R/bwzBiMESBAa0NPAmBvfXwZCBEiReOI6eDi2DSA6c3oHSijyEHraDu2u9rwZBhZ1nN028+mECqoVKFIvy4ebYHZNHPIyuhO4v/GowwopPwk7pyvwRZZAiS5SCQiEHcycvgpFB+18Ard1c4NqnNxIjomCgMGYFTQOUVJcmjodzv0Do6BoApe2vMKvCzNgClSiHQpC3KZzY/aOYGT2TSXFH2HtiO8QKdfgIgWwisVIogyksVAqRdJ6x0hwZ2R3zAXrQGB4wDt/HfsIEYvIobE6RkIsSFGB2/8fv2PFxOJxbA69HOddi4ZMDkJJciOXvn8H/VpWjv58mMrIVCAqtQc+eVvj9z/kdvq+ioiqkpRZBV1cL7t1Vf1bfjwwf6YE/Vp1CRaUS+notBUGpVImtu6sxcap/u7ffvSsS1TUyvP2ibZvXTFcixktLDLFoeTxyc8pgZW3YeFliQj5++vEUvnjHFK8+Z9J4vqe7JiaN0YXvyAxcrjkHmXoNRJoKrP5zcZemV1Ma8cRhM7Dv5HaUoxTWgiPUoc7qDKpJXezdMaL/uGvex7ghU7B6yw/IRipsRE0NaqojY0SXoK2pg0F9hnX62MxMLBvrTlViIW3fKKGEucm1E5kbiEmKREzSFbbNQjYutEGkBnUYoOl1b44pLBEvRLD1ZEM9o04f//2Mr1cAS7ROrr0KbyGgxe88Cdvp4nj07NYHJoZNIZEczn01Ubh792707dsXDz/8MCwsLODr64vVq1ffyofk3ENYWRng6KmXsGrNXJhYuiKvzBJjJvTF8dMv4r0PJ3aouDp8KAYjh/4P/ft+hXGjVqCX5yeYN/sPlmh2v0OFztPPDsX6LRVYub6M+T02EHFVimdfL0T//o7o49d+FzwnuxS9ezQJfa3p462J2tK2K76nQw9i/iy9FiJhA88tNISJkRhVWmWYNWE+Xl/8CeuYdiV6En28u/RrDPIfjgz1RITiBCJwHlrmGnhu7msYN3jKde9jzqfvQMdCHxdEhxElhCBZiMEVXGAr1FauTvB/5MYEoyH9RkOurEMa4ttcRuIpTUIOCxjLphg6IoIHXT4Na6UjEwkJNaihDu2HzdShDlqa2jd07Pc7lB7XxysAkaIgxAmXWbe/TChGgnAFEaIL6NXdD/16Bt7pw+RwOF0Mr0c514LqzY8/ewhHTy7DyLF9WD1qau2K1Wsfw6HjL8LCoq2vbWtyssuw+ImN6OnxEcaOXIHA/t9gUMBX+HdbOB4EFjzeH3KFCHOeyUVpWZO1CgmHC17IQ0mZAk8tGnTNetTMRAN2NqqTfX29tclaGjk5Laff/t54EWYm6njhqbZClJuzJp6Yo48q9SJMm90Dx04vg19fB3Q1j0x4HAunPwelkQyXcRahOIl8rUyMHjwJrz99/RqYNmCG9RuLaIQiVHQSyUI0q1HOiw+iQq0Eyx5/u9MegoS1uS3cHb2QKopjomObIQtRNIz0jNHTvU+H7i844jR0xQYwhVVjPaqEAgqotihqqFW1uvg7wP2ApoYmFs54FnlCFsJFZ1AgZLNBgCwhBaHiExA0lJj70FN3+jA5Dzi3dKIwOTkZv/76K5YvX4633noLFy9exAsvvABNTU0sWLCgzfWlUik7NVBe3rFRaM69i6amOqbN6M1OnYWKryWL/sbwQAl2/GkNJ3sNXAitxbe/JmHCmBXYf/h5NnV3P7Po6UFIjM/Hs68H4dtfyzCgrybSsxQ4faEanp4WWP3n/GsKrmZmeohLKmr38thEGTT1LFqcJ5fLUFJWhr6926YcE1paYvj56KC02Id1WW8V+roGLLRj9qQnUFiaDy0NbZibWHa4e29sZYnlm9bgwvbduLjrAPJKMmFkZYE+wx7H7BnzcSUlHyjrvCeMhYkVpox6BDuPbmLCoC2coQFNFCEP6eIEGBmaYNLwjr0uCoUcMnldi+AWc9giCVchFWqYIXVzqAuZL8rEhF48jEMVJM4+P/9N7DzyD46e24eM2kR2PnXrA31GYOa4eV2yIk/+nVdiQ1FbVwtbSwd4uHg/MFMlHM7dCK9HOR2BwjKuF5ihitzcckwcuwIKWTW+fMcEwwZJkJsvx2/ryvD0U3+joKASTz8zGPczdvbGWLvhcTwxfx3sfVMxboQE1A89dKIGdTJg1ZrH0M29ZT3ZHDNzPRSXylFQKIe5Wduvp3FJdY11a3PS00vQ00uT1Z7tpVb/tKYMn381DTo6qkXIm4U+30cMGM+awHlFOZArZLA0te5wk5xuT/Wsj2dfVpukZ6cwIWlYzzEYPWgSLK/huX095k1djE9+eR2h8pNwELrBAMaoRiXSRQnM63vZjLc7XPdQSKCWoNNYz1BISizC2SSkKjucLKTAxMCMTV1y2hLgM5g19rce2ICInPON53d39MaUUY/C2rzz/xa1hvzQoxLCkV+UCz1dffT28L8h0ZnzYHJLhUKlUsk6uJ999hn7M3Vwo6Ki8Ntvv6kszD7//HN8+OGHt/KQOPcJNTUyvPHqv5g1RR8bf7ZsTFjr5aWFhyfrof+ETHz43l6s/3sh7nfh4+v/zcCs2X7YsC4YiUkF0DfQwS8re2PylF7Q1r52UfTwo33x2sv/IipWCm+PlgVNZIwUh05UIWBOy3UHNTV16GhrISVNdYoxdSkTU+Rwsr49K+D0gUf+LjeCxEAfIxfOZacGQlIzoKl1c93P6WPmwsjABLuPbkFY+Sl2nppYDf18AjFn0lPQ121am7kW6uoaMNI3QVlFEWxQ/xzpZzricRnn0EPwh56o/r6qhQpEi8KgoakBvx4DsO3gBlyKCmaJek72rhg1cCK6O/fAg466mjoTBB8a+QjOXTqBY+f3Iy07CacvHsHZ0GPo23MgZo1fAMsb8Cok78l/9v7B7lOhlNcbkEMJazNbLJ69HG4O3W/Jc+JwONeG16OcW8k3Xx6BtKYKFw/ZNU7E+fTQwphhEix/rxAfvb8XMx72bSNy3W+MGNkdwZfewMZ1wThzOpHVg4ufccW8BQGwtbv26inVrG+/sQvfryrFp2+1XLeUyQT8b2Up+g9wahNyaGIiwZUwOduqaZ623EBymgwSiTq0tLrGJ/t6NTlN8d0IJL719R7ATl2Jk50b3nnuK/y9Zw2uJjWFljhauWDRxOfRs3vHpgkbfPXCRSFQKBVQE6kxj0MbwQmJiISGoAlL2EMsEkMuyJGGOOQiHfNHLMH58FM4HXIExaWFMDQwxuC+IzGwz7Au3za6F+nt6Q8fj75IyojH3uNbcSXuEuJSovDV6ndgZ+mEaWMeRb9eN7bpcik6GGu3/YzSiuLGepSGKmiYYdLwmbyBzbmzQqG1tTW8vLxanOfp6Ynt27ervP6bb77Jur3NO7jkKcPhtObAviiW+PvRa00iYQMmxmp4+RkjLH0zBvn5FR1aGbnX8e/nxE6d5eFH/PDH6nMYMyuHhZKQyEps2VOJ1z4ugqGzFVwCAoHiptvQB0t/n+H4feNxLF9iBEODlsXX7kNVSEmXYs7Ezvup3C/QazRywAQMDxjLOsN1sjqWjGeg1zGBsDnD+4/DnqNbYC+4MVGQEtJ8hcFs1ToIRyAR9KEuVmfeivo6hnh0/EJ8vvItyGVymAs20IAOIksusxXmKSMfYSIZB4hOjMCf23+GPoyYKTcFxJQLxYiMCkd0QgTef/4b9p51hj+2rcDZ0BNwgSds4cImSaljn1QUhS9+ewsfvPAd7Kwcb9lz4nA4quH1KOdWNq63bQnDK88YtFmbpVrgnZdMsHJDObZtvoQlzw3B/Q75hL/82mh26gwmJrp4cflIfPH5YdTUCli2yAgOduoIvlSL978qxuWoOmzf1TYUZPpMX6xdcwG7DlZh2oSWQmx5hQJr/q7A9Jl9OmT3cr/iZOuKt5Z8hoLiPBSXFkBfz5BNq3VWKBrqPwZ7jm9lIqAL6v897Q5fyCHHVVxEPCIggR6qRZWQQ4ZJwx7GhfBTSEiLganIErqCAYpLirEmdQWOnNuHN5d8yqyEHnSqaiqx6p/vUFRcCHulG0xgwda2s/JTsGLDF5g35WmMCZzcqfuMjA/HD39+yjzcKQjRQGTMgnHSZQnYcmAdS+mm7wQczh0TCilhLi4ursV58fHxcHRU/UVJS0uLnTic65GSUgRzUw3mP6KK/n7arLuYmVHyQAiFN4pEoontu5dg2XObsXBZLBYuy2u8zHtYN1gtngv1cvo72XIFl1aKQyJPYdTDOfjmfRMMGaCD6hoBG7dV4NUPi+Dj4cun11h3WY11c2+G8UOm4uKV87hUcJoFt5A3DBVgZB5di2qY25jD2c4N7s49mMfea189DR2ZHnoJA6Ahqv/7ISgFVtjtOraZHU9Dx5pWEupkUrb68CAV0TT59/uWH1kBRa8TdcAJI3p1lQ4IlZ7Ext2/45Un3+/wfWblZeBM6DF4oA/sRC6N5xvDHL2FwbioOMbW0Zc+9voteU4cDqd9eD3KuVUUFVaiqkqGgD6qvYFNTdTQzUUTKant27xw6nn5tVHQ0FTDD98dww+rS0E6FvkSOjkZ4+8tczFgYNNnawMB/Z0wekx3LHg+AbkFcsx/2AASHRHOBtfi1Y+KUF4pxtJlD27jujlkz0OnG4U2LaaNno0dR/5hab204aIBDejBAMUiDWhpa8HLoyeszGwxxH8Udhz+BynpieiLYTCCGfCfLlmOElzOO8fqsBcff5udR9OntdIaaKhrsG2aB4ndx7agsLgAfZXDoStq+s5KzX4SX//e8zsCfAJhqN9+SGNrNu/7E4YwhY8wsFEQpglQSqgWCSLsOrqZbRrp6tzfU86cu1gofOmllzBw4EC26jFr1iyEhIRg1apV7MTh3AzGxhKUlstRUqqAsVHbdYLUjPq1WCNj7sNwPWgV5q/NTyIpqQDBF+rTch161yHNzBdHknTpE11lsfDG4i+wctOXGDEjCxIdNchkSsgVAgb0HoKFM1+4ZSPt4fG5nb5NqrIWVSIlatSVsMG9lYpNq9XvPPsFthz4E2dCjyNZHs3ONze2xBMjlzID7IbX+uj5faiprUYfDG0UCQm63AkezCfx4KmdsLWwx+7jWxB0+Qzz0qEU5qH9xrBVhBuZerzXuBIbhrLKEgRgVKNI2ABNbDoq3ZnHYHFZYYcT586Hn4SmWBs2yrbCAwXR2CidcfHKOUjrannYDIdzm+H1KOdWYWCgzQSt1Ix2Ah3qBGTnymFsxOvR60G1yrKXRuDJRYNw7Ggsystq4ehkgsDBru02M+k2lEr9yovb8MJbl/HiO4XQ0hShqloJt26m+Hf3Y3B1vb/9ym8n00bPYXXR7mNbEV5yhp2noa6JIX1HYtb4x6Er0Wv0aiZ7F2fBE0ailnUUTbe5Knsg7GoQsvMzERZ1gXkzFpcXsvezV/e+mDxi5gMxcEAN+1Mhh2GjdGohEhL0WrgIXsgWUlkjmmr0jpCTn8ksdXqhSSRsDvlJpssT2Os+xL9zk7+cB4tbKhT6+/tjx44dbIXjo48+grOzM77//nvMndvkB8bh3AgTJ3vjnTd3MaPoN5eZtLiMJgl//L0MvX1t4eLCY+U7ChVSDcVUWlUk0iquv8rw2fKViEmKRFp2MisUfDz8bqpbeT12xKYiz1AOPcOOF9wFhtXsp56eOmxMDdDP6d5bH6PCa+GMpXh04hPIK8yGhoYmWxtpXTjHJkexgqx5+ElzLARbxKVexrvfvwixQo0JYmzltrYYR8/uY0IWrdySv+L9TE5BFtRFGmztWBXU+RYgoKAot8NCYUVlGXQggVik2geJ1nFo1YOEXC4Ucji3F16Pcm4VBoY6GDXaA7+uS8XCRw2gqdnyi/mGbeUoLpFj+szOh/Y9qOjpaWHKVJ9Obcf8smoO3nxnPI4eiYG0Vo4e3jYIHOLKfdi6GHo9KbSFBKacgkzIZHUsbKV1QEZSehzzaraC6prbEnaIQRi+//MT5BXmwFKwgzdcUCfUITk+AZ/FvYln5ryC/r3v73X9yupyVNdWwZAmLlVATX99kSGr/TtKeVVZY92pCgpCVBdpMjGXw7ljQiExadIkduJwutoDZdHTgXjvq9NsLeGZxw3ZZGF8Uh3e/7oYp85X46/Nj97pw3wgCgYvt17sdCuhKcJg/UrU9AT07KrgZJXQ4duSc6Mgqi84PST2d0SUOnZhPxJSYpiw18vDj3kX3ogYR4XYtVaZaXXj2tQXzFoyHfTBECaWEVTIkS9KWPlJrN+5Ei/MfxP3CiS8RSdeYSvU5P9nb319r06JjgQKQc48YGiCsDW00k1odyIZzsTIjK3iyAVZ4+vanAqUMjFdl/vxcDh3BF6Pcm4V5Mf30ISfMWVBDr5815QF61VWKfHnpnLm9zzzYV+4d791TVROPRR0svDJgXf6MO5aaLX3bNgJhEScQY20BraW9hgxYALcnTw7fV9Uz9paOtz0MeUWZMEPQ1tMHdorXXFVdBGrNn8Pb3ffe8bHkGrwhNQYFJbmQ19iAE+3XixA71poadWnSEuF6nbvs1ZUAx1t3Q4fh6lR/dBHBUrYWnhrqoVKyAQpTI35pC3nDguFHM6t4r0PJ7KfH313Fh99VwxDfTUUFsthYqKDlWvmYvSYzn/wce4+GqYI1by04e8RiaGm8XA36HgibW4tJbGk4VSJO3IUsSiTejRe5iG5tb8jlKS7ZusK1hE0VlqwxLFdGZux78Q2vPTEe+jh1vGOeUfwcPFmU4G1qIG2SKfN5fmiTEAAuqFXGzFLR6QLB2V3topQWl4CI4OOe6HcCZRKBf49/DcOnt4Fqay28Xw3Bw88NWsZK4Dbo0+P/lj372/IVCaz4JHWRVkmkmBpagMHa+cOH0+g3wj8e/gvpCOh0eS7AalQiyxxCrsO+e9wOBwO5/7Br68DNm56Ai88twm+I9NhaqLOhEJK6310jh+++nbGnT5EzgNOflEuPvvtLRZmYiqygqaghcs5oWw9eMygyXhsyuIunb50degONbE68pQZcET3NpfnIZP9NIdtm9VkOo5uQi/kKzJxNuw4xg2egrudqPjLWPfvL8gtapr8M9A1wsMT5mNYvzHt3k5bUxu9PfyRGBcHW6VLGzucfGShVlmN/r0Hd/hYzIwt4OnaC+nJCWyTSE2k3qLGTUY0JFq68OvRv9PPk/NgwYVCzj2LmpoYH34yGc+9MAz79kSitLQGTk6mGD+xB7S1+Zfx+wlXd2vUmSpgoK3FREJH3Z4dvq2jbv0q9ST7PggvSYKnsQNk0jps2boH6bnB0NbTQ6+RQ2BkadGlx5ycEY/ft/4IG8EJ7kJvqP23kioT6hAlD8b//vgY3765ulPmxNeDhKhtBzcgWnqRhXQ0iIFUGKQhHiUogBjielNpFZjBEvGCEjkFGXe9ULhu5284ceEgHOAOWzhDE1ooRj6SM6Px8c+v4aMX/wcLEyuVtzXUM8KYwElMZBQLYtjBhb1WUqGGFVAFyMZz417rVNFMhRklyFFgCSXLUepxwzGlieOgLdHGlFE8YY7D4XDuR4aP6I5LV97B4YPRiI/Ph66uFiZM7AE7+7v7s5Rz56F6kTYjyJ6km6Mna/p2pWinVCrx7R8fora8BgMwtn4llYJiKPQRSTh8bg/srB0xPGBclz0m+V0P7DMMQWGnYSiYwUhk2nhZhVCKJNFV1rimFWRVaIm0mZdhRk69d/rdTEzSFXyz5n0YCqZsW8cQJqhGJdKq4rFm649QKOQYOWBCu7en2vDjuNdxRXSBCaTkVagUFMhFJuJFl+Hj3hcu9u6dOqY5k5/Exz+9hjDFKTgK3RuPKQOJrMZdNOVFaGrwwC7OteFCIeeeh1KN+aoBpzWFhZWoqZbB0qrlysKlw+ex9vX/oaq8AlrqOpAp6rDr2xXoP20ypr/+Ypc9/qEzuyER6cFD6NOi4KPpQm8hAGfl+3Ey5DATl7oKWk1evvBdfLPmQ5yV7Ye5YMsS6YrEeahSlqOXR19ExoZBATnU0VZMl6Gu/hjv8uIhOz8Dxy8cQHf0hr2oaRXbgjrTSjOESI9hz7GtePLh59u9j0cnLmQm0hQAk4IYaIm1UaOoYuvBCx967oZ8caaPmQt9XUOWYHex8vh/54pYkTd/2tON6yAcDofDuf/Q0FDDxMk9Ub/vwuHUQyFm5Acn0dFtkTJLgWkr1n+BxPTY+gA6ESBT1sHO0hHLFrwFK3PbLnn8qIRwVjdR+jDVpQ1QbWoPN5QIhdh/YkeLcLyuYN6UxSxYIzT9BExhCT3BEFWiChQiB3YWjsjMS2usO1tDDW6ZqA6aGk3BfHcr/+xZC33BGL2FwMaJQPLA9kY/iAU1bN63jjXy2/OnpunLlxa+g9/+/hYXag5BItZjvwc0WODnNQBLZi/v9DGRj/w7z32JDTtXIjItqPF8CxNrLJ3wOgJ8Oj6hyHlw4UIhh8NRSRYzue24J8bdwtEjsfj2y6MIDU1jfzbQ18aUuS6Y/UogEoJS8Mtz62EuWLM0MF2FPvOUyxJSEPzvHgiCEk7zZnfJcUTFhcNcaauy6KKC0ESwZNfpSqGQ6O7ijS9f/RXHgg7gUlQQpLJKeNv7YPTAiTA3tcJLnz7BEtQo9aw1WUiBoZ4xnG3b90G8G6BVFBL2bJVtV4PJc5DS42idZsH0Z9r1hxGL1TBv6tOYOGwGgiJOo7K6AmbGlujvM5gV8zcCvddjAidjxIDxSE6PR21dDWws7Nm0IYfD4XA4nAeHotICZpFy4dJJyBQyiCBiDdvpY+Ywe5QvfnsbpcUl8MFAmAnWbMKuBPmIK4jAp7++ic9eXsGajzfL1YQIJj4ZKpum+ppDPtVXii6gpKyI+S13ZfP67Wc+R9Dl0zgVcgRFpYUw1jfGQ/1mYFCf4fjuj4+QmZzKarnWtTJtwFQpK5hVzN0MCbApWQnsO0XrtWHCGR7IlqYgPDrkmg1oH4+++PG99QiNPI/MvHRoaWjBz3vANW10roeznRveW/o1E2sLivOgp6sPJ1u3dhPEOZx7UihMr74KPbW7v6PA4dwv1Pv6mUG/zob9OS+jCE7iO5/UWpRajKCEbJTbmsC7p01jYVFZUYvz55Jx+FAM1q0NgonYDD3gX7/6WZGPTavjcf7UJ5BrKGEAYzbR1/CBTiunjnCHSBAhaMdemI0dBRhfPxDjetAKCa35tgddRusgtwIq9B4eN4+dCLlCjitxYUiLvAB3Zy/EJ0cyfxpL2LPXUCEomLdeNlLx2IhFUFNTndx7t0AeipTW3F7CsB4MIZPXsaATfd22Rs6tX6sJQ6d36fGROEmvM4fD4XA4nPuPuuoqpGdlQlutAM723ZjXXMMkXGJaLFKzkrD90F9QSOVwULqz2rMGVUiOj8fHCa9i9KBJyCnMQn+MgZ6oqU4xgSV8lYNxofIQTgQdwkMjZ930sVITnETK9qYFG2pVqlu7GnV1DQT2HclODdA68emLx9i689XECJZ+7Cb0ZI1eev1KUYhocSicrN263Mu7qykpL/6v7lRda5L/txrUG693LcjDeoDv0C4/RmsLO3bicO5LoTCo0hnauPMiBYfzoJBVaYboPBtUFJVDJ0+MHmJt+Lqr9nu7HRQkxyPk23UoSkjEH+yco3B3t8S7H47HpbAM/P7bGVRU1q8v2MAJnkq/xoLIFFawUjggLO4k5Ep5/SqAiq4f+dwli6KRcS4I6O5/08fczdkLibFxcFZ6tinOKHG3WJQHf5cA3GqoO7l2+y8oryplPokkCpLAFiWEIFEUxYqYKlE56pRSTBo+E2MCH+rwfZPQmZGbypL0LE2tbyjJ+UYg/0TyWiEPF1ViYSXK2AoxdbPvR6gzfCL4IFIzE6GupoHenv7MC0hbq22ADYfD4XA4nK6hrqoGwT/9jbSTZ6FUyNl52po6GDVoItvo2LR3FbLy6gMttKCNfhjF/PYaoI2Hy6JzOHpuPxMFm4uEDVAYHYVQnAs70SVCIfkeHji9ExUohb7IqM3lFJhhrG8KE0PVE4ddBa1a//LXN4hLiYKIiZMCOz8H6chFOgxFppCJpKhUlsPR0gUvP/F+p6bfiksLUVCcC4mOHuysHLt0jbo9jP+reytRXu/9qCJhmOx+Gq53v1Enk7KJ0bCrQairq4ODjTOGB4ztsrV5zp3lnhAKY4utoCnlX4A49y+1ZZWQ10ihY2oINY274a+lNfTr9GAqV4Mu6zBW3fYjoBWI/OJcpCVEIXztP6xb1wsDmO9HNSqQnpCAebP/ZNd1sNWAnq4a8vKUcEPPNsUBFUbWSidm2qwF1f+WUCqYplgbdVVd81zHBk5GeHQwUhALZ8Gj8ZioYxuLcChFCozoPx63kojYUPy44XO20tIf/mzSrhbVSBcS2AShvYMDzE2s2GrsEP9RsDSrnyDt6PrvjsN/s/eIoOfn6xmAuVOeajdEpKsY7DcSe45vRSZS4ICWa9J1ghTZ4lS21tLe2vGtmHA8dmE/gi+fQY20mq0b0/qxf8+BXb7iQb6Wa7f9xDrURoIZFCI5LsdcxI7D/+C1pz+CvdXNT8NyOBwO58GkpkaGwoJKGBpqw8CQf/dqeE1ionMQmVeIve9/g9KkPLgoPVhirwAlcurSsPfEduw9sQ3mpmpwddREUlodnOHZQiQkqLnpKnjjouI4m/JrD21IUFKT1yXH79sjACYGZoitDEdv5aB6P8T/KBCymVA3a/B8Zslyq6iuqcInP7+ByrIK9ER/mIPqTQF5yEKi+Ap0JBJ4uHmyBq9fjwHwdvftcP1Eq7Ubd6/GlbhLjeKjtZkdZo5/DP16BeJWQvWes203pGfHw0ywajGIQNORqYiDjpYufL364XZA3ttkp0PTqHkF2cxKZ0CfoRg5YHyXrLE3J68wG1+sfAeFpfkwFplDXdBAfFI0DpzagbkPPYWx90BaNefa3A2KxHVx1+kObcm955XG4VyP2PPBOLJmPVIuXWF/1jHQR8DUiRj91ALo6LftTN1OQpBxRx43rzAHf+9djcvRF6EU6j/wNdXUYK9wg4WovkOlA11oCtooQSGUUKI62xRVQgUkUGerC6owhjlL+6IUWvr/1lBSbY2iEnqWll3yPHp0640ZYx/D9kMbUSDOhJnSmh1rvjiLpesuefRlmJt0zWOpggqUzfv+ZM+VBNYGoVJbJIE7fFgtlZqVjFee/LDTnnyHzu7Gxl2rWHgIJbxR15zMsGNjo/Bh2iv4cNl3t9SXj1YoKEGOAk2kzRKGi5CHFHEM1LXUuqQL3xHSspOZz1BtbS2bADCGBXKqsvFT0hesQH12zqtdtsodlxyFNVtXsOlXd/Ri4jZB60yR1UH4atV7+OaNVe0aZnM4HA6Ho4qc7DJ8/eVhbN0SjtoaGcRiEUaO8cBrr49Gb98b90m7l5HJFPjmyyNYu+YcSkpq2Xn0cW6mtIc9urEtDYKEP/LUK0cJpCW6KCkigTVPZa1JGIpMoCaooVJUymo1VZNv5aJiWJh2TdOVmqYvLnwHX6x8G+elB2Eh2LG6rVRUyGrivt4DMH7INNxKToUcRlFpPvoLY1oEqljDAXpKAwRXHoW3ex/WtO4MOQVZ+HDFK0CdCJ7oAyOYsoZ4RmESVmz4Ak89/AKG9huDW8nsyU/gy1XvIBxn2WAArZnT1gs15GlS8vGJz96Wukwml+H7Pz9hVkMmIgsYC5aorazGzsObcOzcPrz17Bew7qJJP7I0+mr1+6gpr2VJ2rrQZ2E8CqUCSYhiwi0NH9DGC+fe5Z4QCjmc+5GLuw/gnw8+h76nLZxfngQNIwnKL6fh7LYdiAsOxfO//wRtvQdLIM8vysXHvyyHsVEdfv7SHIH9tJGRLceK30tx4HgIlIIctiIXtj57GefYmH9vDGKd1zhcZusT7RVdUtQXeTRtZqd0bdHlpdsk4So0tLTgENh1xslTRz2K7s5eOHxmDxJSY1i3tp/HQIwOnAxHGxcolQqER19kk3/0oets54pBfUbccJhGc7Ly0tlaML0+ql4P8mVMlyfg0tWgFt4x16Oiqgyb9v7BkvJIcGy4b10YwFxpg4s1x5k4+vSjnU9p6wzzpz7NuqMHT+9EWl184/nd7L3w1Kznb6kI27xz+7+1n0As1cBAYViTSC3Ur/JcvHIOB+27scCUrmDfqR3QFxvCQ+nb4j2l9XFvZQDOVxxEcMQZDPEf3SWPx+FwOJz7n8yMEowb+zNKa+Qwm9kfut1tIM0txfm9lzBh7C/YtPUJDBnaNgDtfoasVRY/uRGHDlzFcwsNMXuaORMJt++txHe/ZeKKvBY+ysFsgiwRkahAGWvKUh1EK74hyGN1J9VGraE0W2ocS4Va5CGThYk0p1jIY43PmQPmdtnzoWCLz1/+GUcv7ENw+BmUS4tYUMajAxcgoFcgq0+z8jJwNuwYSsuLYahvjMF9R8LW0qFLHv9M6HE2gdlcJGy+9UOpyLRq3VmhcPO+tRDqAH/l8MZJSXrNKTAwGmHYuGs1S/i9ldYsnq498eqij7Bu+y+4VHi68XwKB1w0Ydltq8l2Ht2Eq/GX4YtAZrvUMLDqJngjvPosfvjzU3z+ys9dspIdfjUY+cU56IeR0BXpN55P4nk3oRfKxSXYd2I7FwrvcbhQyOHcAapKy7D1s29gNqonnF6cAJG4/h9tw76uMB3pjdiXN+Lomg2YtGwJHiS2HlwHA30pgvbbwtys/p8nr+5aGDNMgsWv5GPDpghYKu1RgGxIUcOm2WhCjrCEHZsYLETOfysNTdC6b45aCtz6OKEguQyhpSfYhKIRzNj9ZIqSUCTk4dE33wR0uraY8HTtxU6qfOa+/v195BRkMvGHVknPhh7H5n3r8Oxjr6KP1835F1ZUlbOfFPqhCi2RDtShgfIqSrfuOOfDT0GpFNhKTetig8RXO6UL8ytZMO2ZW1qYUVE7Y+xcTBw2HTFJkcwnhQpa8qW5XYTHhLAOORVKrSdZafrVSnDEoTN7MH7I1Jte6SExOzIuDE4qPC8JKr7p9zkiNowLhRwOh8PpMO++swflcgEeKxZC07TpS7/Z6F5IfH8rnnt2M8Ij3oS6+t0ddNaVHD0Sh727o7D1d2tMn9hUR/n21MaooRKMnJHFpsVokyALKXCCe+PWi75gxDZfMpEME7TdrqDr0+d4H6/+rFlbKhTACg5sFZmEwyxRMrzdfDGgd9cGW1B426zxC9ipOdS0Xvvvz2xLQ0tMrXd9ZvGz7+R2DOs3FgtnPHvTNQw1mY0Fi0bxqjU6gh7KK0o7dZ/llWUsTZiEqebr1AS9vq6CF87VpSHkyrlOC5CdhUJXvnztNySlx6GwJB96EgN4uHrfNgucOlkdmxq0EZxhKrJqU++7K31wKf80YpMjVX4n6Sw0tagvNoKBYNzmMnrtrZQOiE25xI5LU4MH0t6rcKGQw7kDhO0/zKaR7J4Y1igSNiBxtoDZmJ4I2rkH4597CmrqD8Zf06rqSlyMPIev3zNpFAmbf+h88Iop/txUjjxkoBRFbLS/eRfLEKZszeMqLsJL6MvEQrodrRQniCJQKZTj8Renw85+KLZ8/jvCDp2D8F/qsLWrKxY++wJ6Dh+CkNSM27Ie8OXKd1BRWgF/jIChUG9yTKJlnPwyflz3OT584Ts42rrc8GOYGtevvFSgpH4loBW0qi2HjK0Il1WWIij8FIrLimCkb8xS19oLJikszoNErAdNpeoVbwOYsOnIsoqS2xKuQY9xu7xfWkNTohI1fRgo2xZKhCVscbn8HHtdu2IVm1Y6SFBuD1ploteew+HcGtKrr0JPjX/p4dw/FBXUYP/eKNgtHtVCJCTEmuqwfWIYol/4E7/u2I5+IyxRXifFg8D3qy+jd08tTJvQdsNj2EAJRgRKcPlCCrQUOiyswgpNTUqqPZ0FT0QjFPFCBJzgwZqJtA2Tg1Qki64yj+p5Uxdjz4ltOHR6NzKrk9ltJdq6GD9gGqaPmdtltiXXY8eRf3DiwkF0R2/YKp2ZjyI12LORwlaG9fUM2oiLnYVqoNLK9oXACnEpXExdWQ1D4l9yRjwT2Xp194ObY5PPd+twFDpOQ6iuV2mQgIJhqDF/O6BjpGOl0+0mtyALVbWV8ITqdGP6fkQ+7HEp0V0iFNL7pIb2fz8balUF1aRcKLxneTAUCA7nLiMvOQ26TubQMFK9YmrQ2wl5u0JxfutOaOvpwcW3F0ztOh40cS9SXF4IhUIJf1/VPh621uqwNtdATV4VBAj/Jaa1/IDuJQxAJIJxBRegAU1WmFHimI6OOj5YsRQGfqYw0LDEsz+9jRMxV2FWagAtPQnMHew7NIpfW1eLMxePshMFWBgbmmJov9EI7DsCmhqqhTNVkCCaV5yD/hgNPZFhi66ftxCAINFh7D/1L56Z8wpuFAoU8XDxRlpqPFuFafCza5hOS0Y0dHX0kZGTil/++hpkB6kj1kWtsgqb9q1lCcgzx81r87ro6RpAqqxhBW+DP09zyC+PuuKUOne/w14bQWh33Z1+Txuv1wWP5WTjhsLsHNjDVeUqU6moCMPtb60XD4fzIBNU6QxtcA9Qzv1DcnQ6lAoBBj6qp/F1u1lDXVcLazYJ2HRJA9q63WDo3g2iLg7qutuIT7iKR4dqt/v5PaCvNoKDKPyu/nNe3KomtRE5sc9lWkvOQBJ0BAlk4jrIlHUY4jcKc6csYlN6U0Y+golDpyMrP4PVEjYWdh2uJ68mRuDo2b1IzkiAuroG+vToh1EDJ8HSzLrDz7NWWoODp3fBAd1gL2oKh6OVaju4olaoweEzuzF5+MMsaORGGd5/LFan/4BSFMJIZNbiskIhB2VCEdydJ2P5Z0+ipLyINaQVULB1Wjf77lj2+DswMmjZlNWT1Avb1ahiTerW0OsvhRR6um2b5fcbDUMnDXWnKih4p3nYys3gYt8N58JOsgEH+u7SmkJRNixNbG7LwADn1sGFQg7nDqAl0YGstBqCUmgzUUjUFVeynzu++qH+DJEIXoED8OgHb0DPRPX00rVQKhQI2X0A57buQE5CMjS0NOE9bDCGzXsUtt1bpsbeKfR06j/IU9JlGOjf9oOlolKJolI57KEFMdTYZCGFgjT/gKLVgz4YjIvCCWYE/fAcH/T1d4TfOA3AqB/CS5Iar6tjrA97W49OrU189utbzPuPphUNYY6KynL8+e8vOB50EG8+/Sl0JR0Txy5dDYaRyJSlELeGPsRpZD8s6gJuBlp9MDYwRZzyKk5gJ7QEbdbxpsnLLCSjCLkY6DmcFWHU7SbPQg2lJiusaIV79/Et0NLSxkMjWoaC9PcZjG0HN7BOM/kUNoc6u5niJPR084W+bltfnvsNL9debDWnDEVs7bc1uaIMWBhbs/ehKxgTOAkrN/8POUiDtcixxeseJwoH1X/DbrFpN4fzIKOv3hc6Gg+WdzDn/sbEgLYP1kBWXAUdx7bhG/KqWshr6pB1LBSgE4mHemboHfgILG09b+gxiwvSkBh1DDkZVyEo5TA0s4erxxDYu/aFqIuEjJtFW66L5LT2rVmSUmUUnwcJDFhzlHyJqY5qjqPIHeqCOmJwCdYOtmw6jjY2WgdKkMhHvtUdhQTFLQfWsZRlWv80UVqyqcbj5w7h2IUDeGnhu+jp7tuh+yLrltq6GhYKpwoKT0uVxTJRkkJPbnT6TFpXCy0NbYTKTkIsqMEMVizIpAwlSBclwMO5J3Ye2QSJXA8BGMXWt+l5Uq0amxXOwkI+fumHFqu8NKVIImJmZiJbAW8tgtHqN8lj5MF4v2NtbgcDXSPkVmWoDNGh15FEavJT7ArIT52skmLl4egpBLAp1AbyhSz292He4MVd0ijn3Dm4UMjh3AF6jRyKkxs2oTQ4AcYDWhYWgkKJ/H2XoCsygL8wnFRC5AkZSDofjl8WLcOLG1dBU6fjEw0KuRzrXn8fUSfOwMjfFXZPDYe8sgZXj4bg8uFjePybT9BjyCDcaWg6z9PVCyt+T8Gsh/ShodHyw2XVhjJIpQLrztZ3zERsraOXMLDFZFu2kMaEm3feHY9ly0ew89KqIkF935vh9y0/oqAgnxUwzacAybQ6PPcM83dZ+tjrHbovKpjUBc12vVoowZfWk9ubVLse6dkp+Oy3tyCTypiYR145ZShGOuLZK2dpYoXnJrzOjJepOHQTebcQW13gxQTDvce3YWzgQy3S2ijFjDxrToUcYVOFVETSbcqFEiSJrrKkt2lju86A+27G290XNub2iCm6hN7KQSxUhKD3jTyISMxeMPQZiLto8mJgn+GITrqCM6HHmFhoKlixLwd54gzUCFV4dvarzICcw+HcGi7mZ0Kzik9IcO4fBEMx9O3MWd1p4OvU5vLCQ1eoG4W+GAoDmKIcxUiuikHQ4d/wxtOfss2FzkAexjv2fgttKyNYzQ6AmkQTZSHJCD21HlqVmVg868Uu+8y8GYzLx+OPbT8iMkaKnp4tJ/zSMmTYvq8CMjlwDvvZeSmIgYlgwYI5GqDP5WTEwNrMDh88/22XHRs1kkkk7IZecFB2a6wTuyl7IlIIYqEV37+ztnHi7lqQvzNBWziq0ED9c5fJ6m7oWOVyGb5b+zGi4sNhLrJhIhaFvGQjlYlJGmoaGDvoIdTJapGSmggfYRDURRrstvS8zGCNXkpthOQdQ1hUEAJ8Wop+MyfMx1er3kWkKAiuQg9Wn9cJUmQiib0nYwOntGulcz9BAurYIQ9h24ENMBbMYImmTSmyGooTX4aLjXuXrUVT6OLSea/jh3Wf4oJwGFZKe/a7UizKQyFy4d9zEEYOmNAlj8W5c3ChkMO5Azj26oFuAX5I+W4f8KIAo/7dIFITo66gHBm/H0dNcgH8MKTxw5LEGEOFCYKTjyJ070EMfHhqhx+L1pevnjwLt/dmwLh/U2qd9ayBSP58Jza8+SHeP/gvdPTv/KrotNHz8OXqtzF9YQ4+e9uUFWfFJQqsXF+G974qggmsWJIXCWnkjUFrxlSkWQkOrLNbLM5FqVCEOY/544WXSGTtPLWlZTiw+w+c33cA8qpqGBtYoKDfUFyKDoYn+rQQCQkqCilgghJuS8qKmOB5PeytnXA1NgIKQd5iJbiBIlEebCw6tg7dGhKpftrwBdSk6vBTNiXxUqaeo+COS6LT6OHeG7rauqisqYA3VKc8k8CYIU1EVPxl+Hm3vM6C6c+wDvjxC/uRJESx31MSFo31TPHyo+/DzaE7HgToy8zyJ95louz5soMwE6yhCW2UiYtQqSxjHkRdWSjR4y2a9SJ6uvfB4bN7kJoVw94H8mgcN2QqSzXkcDi3Dned7tCW8IlCzv3FpMWL8c97nyJ99THYPDoQ6vo6UNbJUXg0Epl/nICtyAVG/00p0fR8b2EQwnAKm/euxfsvdFwAI8sWmoo3GeoJ55cnsbqXsJzij6KTV3Huy90sFILSdu80A32H4vDZfzHukRz88JkppozVA+mXB09UY+kbBdBQasMR3VgoHHlmxyAMITgGM8EGBjBiTdM8USZMDE3x+tMf39AxUMhI8JWzzD8wJz8LEh0J+vsORURMKIxF5m0mGKme9BT64px8P7PIGT902nUfw86qXhwuRh4Tl1pD59df78aC4vaf2oGrCZfRmyXxWjY2yMnDMUJ0HrUalSyY7qVPn2RiU8P3nuYYiIxBucjBEafbCIX0+7JswdtYs/VHBFUdYYIn+W/TWvf4wdPwyISb81a8l5g0bAYyc9NwIfwU0sTxMFCaQCqqZgnaFkZWWLbgrS6d8KNE44+WfY8Dp3cy8ZrEZPp9mjboUQT6Db/pABzOnYcLhRzOHYD+oX7864+x7rX3EP/Jv9A00YdYSx21OSW0Zcw+EGlSqPlEGQlU1FkL2XWgw0Ih3f7M5u0wHuzRQiQkxBpqcFg6Flfm/8zEx8GzZ3b586THp1Tf6poqmJtYXnfaiUbiX1zwLv7Y9j16j0iHgZ46qqoVUAoC9AQj+GBgi9WCAGEUohDMOocGhtro2csGTy6eiAmTvG/owzA3KQUHl73OJi7NlbagZd3S6iJs2LWKXW7WKk25AUpcjhcus7Szvj0HXvdxhgeMY93gRETBXfBpcazk1ULJzY8PehY3Ak2c5RRmwQ9D2yTxkqhpL7ixiTQ3x3oxTwuqp2Mazq+RVqvsXC6YtoR565CAWlNbzXx1aK3mdplv3y3QhOUXr/yCs2HHEHT5DGpqa+Bt5cNEQpq06Oq1C7o/Wl2iE4fD4XA4N4v/5HGoKi3F3h9XomDvJWhbGaM2t4SJhdT8kkAPMtQ1JstSHeYgdENkRhDyCnM67Il36uJhCGqAw7NjGkXCBkyH9UDxsas4fH7PLRMKKbitoCiXee1drxlLPoGvPfU5ftv0JR5ZFAVtbTFbMa6pVUBDTQw/xXDoiZosVvoKw9gEWxriUa5RxCxHZvSbi5H9J3TYlqb1uu6P6z9HeHQwTEQWMBYsUVtZg91HtrAmswOJhCoOX0ukzUTd+NToDgmFtpb26O7UA8npMTBWWrSoG2kyL1kcDTc7D9bgvhGh88i5vayZbyqybHEZbQJ5CL44X3sQwRFnWa3ZXj1KaAraqK5pW48SfXoEoJfHOlyOuch+H3V1dFmDW1+3rb3P/QwJc8/MfgWD/UbiRPBB5BZkw0xihsm+MzGwzzBoN9sO6iro92LxIy8CdOLcd3ChkMO5Q+jo62PJr/9DWuRVbP3ka2THJ7HAjn6+WohPrMTl+HMwEZuhl7JpDF8i6KG8sLjDj1FXU4PCtEy4zJqs8nJNEz3oudsg42osuprQyPPYfugvZOalsT9TQebrGYA5k59kRWVeYTZOBB9i3S9NTS3mfUKj6tSh+u6tdbgcHYK8ohzEltagTlKJ6CN7mXeeneDKCgxae6UuWaWoDC+9PAJvvjPupo5XqVTijxffgnqlGP7KsazYaoCEuwicRzoS0A1t/T0azYM7KAqRaDpvymKs37mSJb2RJyF1pelx8kWZ6O3hj6HNvOZIcCWxlV5DKnAT02IRnxrDHs7LzQdOtk3hFikZCayYNxLMVD82bJAsj270ASJjaTqvNaUoYD+tWnnpNIeMpUf0H9fiOCnN+0ETC+k9GT1oMjtxOBwOh3OvQZ7VfhPHYv+KlQjbdxDqIiVGjpBArgBOnI1EmhCNnspBbJKN0IV+o/jWUaGQ6hM9b3uo66kWLAwD3JD2y2FWj3Xl+jF5Nv+953e2ukqevgStA08fOwf9ew9hQXUXwk/iSmwYs31xsXfHsIAxMDE0wxuLv0BadjLz8hOUSrbGunLT/5AoimRCFyXrUu1TiTLki7PY5Nbnr/4CDfW2k3GdYe/xrYiIuYjeGMSGBBpEQangjUs4zdKT3QTVzcgGe56O8uSsF/DxT68huPYobJROzD+bnk+OOA3qWmpY9MiyNvY50jopdHX0UFFVjtCoC6iprYKVmQ3bcKBNB6K8sgylFcVwgOp1V4lIjzWwKYyFbluaV8RCVVpD71m5uAS9LHq3+xyogd3aQ5HqUfo9epB88ui59uzeh504nJuFC4Uczh0mIeQSchKS8OtXFnhyjgHU1ESs6Dh2pgYzn8hBbE0ovIX6D79KcTmMrCw6fN/i/wQbRa2s3esoa+ugptG1/xScvngUq7d8D1ORFStyqB9dKhQiNjYKH6a+jOEBY7HnxDZoiDXZaLxcJGOru/+a/o03nv6EGRQ3TObVxaZC188UMo06JOw/jHS1WFZcVAtVkCqkeHzhALz2ZucDHGpKKnDq380ozs6FxNAABubmKMzMRF8MbyESEmYia9gKziwEhDxQWhsm5yEdamJ1uDt5dfjxSVSyMLVmk4WxyZfYeebGlnh00EKMDpzMxDYqliko5dDpXcgtymLX0VTXQp1cCjX651sE1lmmbjB5hVABS34zdB6lm4nQVrCjSVXCztIBLnbdkJIVA2PBvMW6B90+WRQDOwtHuNq3XG1RRX5xLvaf3IFzYceZKbaRnjGG9R+HcYOn3FAnncPhcDgczu2lpqIC4QcOY/wIHfzxPwuYGNfXELn5csxenIug0HMIUIxjNVIF6oM+aLW2o5CApKxp3+tOUVvH6tauFHaKSgvwwY8vo666Dt2EXswjrxY1yCxMws9/fYWMnFTmuVxWWQJjkQXUBDGuxkVg97EtWPzoixjoO4wFjTQPGyHvvxUbvsA56QG2FqsQyVGpLIeNqT1eeeqDTouEJGjR5CBNAVJh192lB7MXsRGcWP3ZHHrtPQU/hOIEC6hgImIzaoVq1uj1cpve4cencJWPXvwf9hzfirOhx1mNqaGuiUC/EZg84mHW3CZoa2bXsS24HBPCvqdoqGlCpqD3UwRNsSbqlFLoSwzx1KwX2JQfhPomuvK/urM1rLkskkNdXR0jBozHhp0rUSIUNIrRDVCTvlZZzbZxOuKJeCzoAI6e24fcwiwmIPp5D8DEYTO4PQuH00m4UMjh3EHkdXU4+/cmLFlgiMXzmkbkqUgaNUSCbz8yw9MvZ8EFlZBBiiIhF6OnL+zw/WtoacHN3xd5x6JgPr53m+KrKjEXVSn58Fx6/XXZjlIrrWEf9tZwhJfQt2l1GoawUNrhYs1x7D6xlXmruCh7NAaRUChIZEkQvvn9A3z28ooW3hYisRh+ix7H0KdsIZwKRU2RNiytDPDIo35wdWub7qWKpOgMXDgTColmKQoyc7H/1y1UvUBXTR+1Qg1kCik7FkpaU4UF7FiCGpkvWzXzcaGiJkUUi0F9hsNAr3NrDj4efdmJxD25XM4m0xpeLyqgVm/5ga200mN7I4B1iXPlaWyS0g4ucBW82RRifHoE88lbOOM5JtgpoUQeMtl70JpspMFQzxj21s5YOHMpPv3lDVyUH4ed0g16MEAVKlhysVRcg+E9R+NE0EE42LowwVBV8U6d9s9+fRPKOiWslI5sRam8shh7j21D8OXTeOe5rzr9unA4HA6Hw7m9nN30L8iu+p9fLaGj09QQtbJQx/a11rD3TUWWIhmOQneki+Lh6dyTNXY7CtmTBG85g9qsYmjbtgyYEJQCSo5ehU/3prqxK9h2cAOk1VL4K0c0NoGpHjUVLBGHcCaO0QrxIIxnwW/UgJULMsQJl7Hyn+9gaWoN11a+yzSt9eO763Dh8ikkp8dDTU0DvT37olf3Ph3yZauqrsTl2IuQSmvZ9Xce3YSi0nzoivVZnXfg9A4mvtm38iBswBAmLDgiBbEs1Kzh9SKv6Kuii5Bo6TKRrzPQ+0g15PypS1gdr62l02I7hNZ6v//zU0igi26CD7Shg1JFIQtuo//vqxwOKWqQVHOVBVy8/OT7LBCPnkcWUmEptF31po2WamUlq4PJqiUk4iwup55jAiltulBjOwfpyEcm2zhKSIth20g+nn3ZanhraBr02zUfIjrxCixEtvCEH6vtoyIj2JbTC/PfqhcwORxOh+BCIYejguryCihkMugaG93S9LXM2ASUF5dj/qy2BsLE7Kn6eOa1fCQorqBEXAgHLy/0GTeqU48xfP4crH7+VWSsPgbb+UOhpl3f6axJL0TKF7th5mCHHkO6TigMuXKWrSW4okebooC8T8gjLx4RTChsnlZM6wdeSn+E5p/AlbhLbAW5NcYOxnjkNV846rZd/22PzIwSPLXoEC4Hra/fxPhvS5jWG5zgAU2FFltjpjUOKgzp2Ci0pDU0oUeQJ2I2UiAR9FElKkcJCthE37ypi3GjUMHTuugJuxrERMIe8Ie1qEnws4YD0oQ4JCASFrBlxZCGUhNhBafwxcq3IRbUmFgXh8vQEnSYt03D6gZNRNKxzx72BCsAaWX5/ee/wfaDG5nXIF2H3jNTA3NUl1Ww4rUBBytnLJnzcgufGhIzf9n4NdTrNOGrHNzoX2QDJ9gL3XCp+BT+2r0az8x55YZfGw6Hw+FwHlTqaqWoraiAjoE+a/7eSuLPn8OjUyQtRMIGaLpwyngJ9u9JR5EyF7Vq1Xh00hOduv/+vQdj2+ENSPpkB1zfnQ5tm3rfakVNHatRq9ILMGHJ8i57PuSfTCnLTkqPNpsiVOs4C15M6DIXbKEjagopog0LanSXi0pYUMPSx15vc98kpNGEW0em3Jp79m09uAGHzu5uliIsYk3afhgJA8GY1VWULB2Fi6zOMxdsGmur1sdfJhThvPggTJSWLMCjUJQDTU1NvPLkh6zxfCNQbdh6E6ROVoff/vmObZ/0EgY0btVQDUrbNhdxAkm4Cg+RL7yFAATjKBMVabpPH4YoQT67nAJMGocDhFJEIQS2Fg4skIS+a7266EPsOroZxy4cQEZNIruekb4JdOokuBh5jp0IibYuHh4/H6MGTmxxnAdP70RM0hX4IhAmsGjcvnZQuiNKFIxf/voaK95bf8OvDYfzoMGFQg6nGRFHTuD4ur8bPfuMbCwROGs6hs6Z1eXruQSJkYSeRHX3VFtbBHU1EQqVueg7cSymvf4i1DXbFgzXwjOwP6a9tgw7v/kRRUciodfTHoqyGlREZ8LEzgaLf/oGaupd99zyi3KhrSaBtlLSbieUkKKWmWS3vkxPbICI2FCVQmFnKSmpxuSJv6GoTgHXt6Yx/5urT62CYYEe3EU+jdejwsUOrpALciQhCi6CJ7RELU2Vc5EBEwMzzJqwAGfCjqG0rBh2RvaY2+8JttZA6w1dybHz+2EkNoW10HYqkEysabqRTiRykn8imWxTEUeVUUNKHfnYUAiMDiSs8KTXnGj+2lKS3bLH30ZldQXKK0uxad+fLFHPRejB0rbJO5EmGJPyo9j04UcvfQ8LEyt229jkKGQXZKAPhrQpZHVF+qw4I5Pqx6YseuBMpTkcDofDuVHy09JxZPV6XD5yHIo6GdS1NOE7dhTGLFoAUzvVwWo3i0Imh56k/ea4vq4YNaJy2Fm74sUZb8PFvq2f3LWghujrT32ML39/F5FPrYS+tz3UJJqovJIBpVSORQ+/gO4u3ugqSitKWCiIwX91Z2tIPNQWJExkUyXEWSrtcTn6Ypcdz4bdq3Hs/D5Yzx4Ii4l9kL8/HLl/X0AfYUhjiAg9riFM4ScMwTkcQA7S2vj2UYO6TqjFEzOeQ2J6PFLSEyBR18bUHo9gWMA45h/dlZBAV1VT0SZQkNAVGcBecEU6EuEkdEckglGNCujJDdmaN7XTiVTEsmY1+WdTLUo1KdWt3t17Nw5k0O8HCYDTRs9mvpLRSZFYu/0nJkj2wkA2CVqDKqTVxmHdjl/ZbRrEQrLqoXVjS8GhsUHeAB2zu9Ab52T7ce7SiTYCI4fDUQ0XCjmc/zi6ZgP2/7QKhr7OcH51MtS0NFAanIB9K1Yi+VIEFn77aZcKaoSVmws0NNWx72gVvLq37RQfO1MNaZ2AJ3/4HD2GDLrhx6FEY6/BA3Fh+25kJyZB01oL3rMXwGf08C7vUOtK9FGnrGWrG8197xqoQX1qmQZUd0jJe48Ku65g7ZoLyM0rh9fKxdCyNER1Uh6kBWWwQ5NI2BwSxhIRyTrMLvBqSm5GGjvNGfoUBvkNZ6dbDfnmmCitVPpR0+tkIligDMVsFZoEVm/0g+Z/HXNaP6FOLQmGtBKihAK6MGTFGQl6F8JPYcbYx1rcJ3nu5BflMJ8cui8rkUPjZWawgqHSBMF1R7HvxDYsnLG0ce1YTaT+n0DZFrpdgvIKsvIy4OHChUIOh8PhcK5HdnwifnpqKSDRgPVjgdBxMEV1SgGu7DuLq6fP4vk/foGlc9sm4s1i5+WFPUcu4tO3hDYbITKZgL2Ha+DrOQgvzH/zhh/D1tIBX726kk360TqrvEwGp4F+GBYwtlNrzB2BwjaI2v/qztZQo7UOUpX1KKEOdSiUXVOPkpfz0fP7YL9oBKym9WPnlZyKhaVg1yJpuAEKSTETrJGJJLaJ0/B+VAuViBVfgoOFMxMFh/cfj1sNhQ5K1PQgUdYH2LTGBJZsDfoqLqIK5fDD0EafQaqhqaaOxSUWgEP1KNWlFWyS0gihVy7gsYcWt/GytDSzwTdrPmRe5z2F/o3PnzZmaKWY7A+3HliPIf6jmMBYVVOJ4vJC9IRqH0JtkQ7zk0zLSury14fDuV/hQiGHQ2EUyalMJLSZPQi284c0nm88qDuMB3si+v2tuLjnIPpPm9Slj6traADf8aPx5U9HMX6kLrw9moqFgkI5lr9fDLvuzkzku1moAz1p2RLcagJ6BWLT3j9Y59ARLX1dqGAgU2JNaLEiqDVkwlwulMDZrnNd6vbYtCkMRkO8mEjYsN5CaLWaZGyARDRa3U1GNOuC0upumagIlUIZBvcdhbGBD93U8ZAAGn41GMmZCWwCsWd3P3Rz9FDpx6OlocUK2Pagyxq64L0woIUoS8+D1kPOYh8rqmh6MlfI+E84lLAuuyrOhp2AjliXddFbQ/dprXTE2dATWDDtWdYBVlfTYOvKVPixcJXWz/e/42tIwONwOBwOh3NtNn30BdTM9eD+1Ryo69bXK0YB3WAxoTdiX/kLWz/9Gkt//6nLH3fgIzPw6+LT+PbXUrzyrHGL2u3dL4uQWyDD0sceuenH0dbUxrB+Y9jpVkL+yF6uPshMToKV4NBmGi4bqcwHzwyqJzRplber6lFq0KrraMJ8vG/jeRTsooX2w2C0oMN8qEPER2GgNIVUVFMfYGJogRcXvnPTXo7p2SkssZh8singrp9PoErvP6pHqQFN9V7r15CQoa5x0tEL/i3CSOgYyVObVo0LkI1ATGDnn8QuJhiWVBaoPLaUzATkFWWzjRVVz9MJ7jhfm4KI2DD49xzYuNWjajq04XdYzoJTeD3K4XQULhRyOPQB/u9uaBrpwvrRtoKckb8rO53btrPLhULioeXP47eYWPiPTcOjU/Xg31sLSakyrNtSBYVYB0tWfdilxs63GhMjM4wYMIGtV9AHsy1cmMhUJZQzj5IKlDCbwEIhF2ai+hVWQikoECsKh5aGDgb6Du2SYyksqITh4KaVE+aHIxKxABJdGLS5PomUJHoFTpmOhKsxqC2vgo1uN0wfPRVebj439T4kpMbgx/Wfo7SiGBKxHitOyQPQzcEDyxa83WZVxN9nEA6f3oNuyp5tJjNJUKXikVa3yfBZ1eQmrVNTp7oQuXCHD3KRzp4zrW2YGJqpPMaKqjLoCLrtPk8J9FkaXp28jhX6Ph5+zHibzKapEGwNFeG0WrJi3efw8fTDmMCH2Kozh8PhcDictmTGxCHzahy6fTCzUSRsQN1AAuu5g5D8xS7W4LZ0afIM7gq6+ffBqKfm4/WP1+Pf/dV4eJIEcoWAv/6tRmR0DeZMfqpF+u+9wPSxc/D5b2/hCi7ATfCGnsiQbbzQlBtZzRAZSGBJws1rn2whle1sPBq4oEuOo6KyDFqmBo0+4YS2gylKSgobvbObQ/VzqbgQ3R17wFDfGNn5mTDTMcUk32ksPI88Em/Gu/GXv79h6cWaYq36Gl1RgY27VuPp2cvh61U/8diAX88B2H74r/9C8hzaHCfZ4JCoWYdaWMFO5WNSuB4NEFBwIU14Uq1NGBuoFkrLK8sa605V6KB+WpQsc9iftSXMLzw3LY2FobSuY8tQhCplOc6HnWBbLmMGTYKvV0CLwBYOh9MSLhRyOAByklKg28MOYk3VfyX0ezsid/3ZW/LYEgN9PPvHbzi3+V8c3LUbf/2bBz0jA/g+NAND5jwMI8uuXcW4HTz20CI2cXb03F42nacu0mR+KvoSAzw3/XWcuXgUEXHnYAZrtkJL03F54gzUiaR4ad47N1UANcfG1ggFSfV+fYSGiR6M+ndDSkgczBU2LXwISahMFEdBx9AYM55/EeGJOdDNVUInqwo9ujUJmjcCpbR9tfo96Mh0EYBRLFmZphRpnTktIxlfrHwLnyxf0cLnkDxUyKcwQnaOFbASkX4LA2h1aEIEscpJvgboMgphSRcSmLBIPi/VQkW7aXgkIFJAC3m9qOoak8hLJtLUXSbMTSzR32cwWx3RErTZ+0nFGXWdaV2GCnESJ7XKdXE2+AROBB9mfoU3O5nJ4XA4HM79SG5SCvtp0Fu1CGjoW39+XkpalwuFxITnFsGxpxfO/r0Fb3x+hX2m2zr2wMwJIzF+yK23Xelqujv3wIuPv4PVm79HUNURZtFSP3UmsHVn8llcs20FCy6xVNqxuqlIlMu8mYf6j0F/n6Yto5ttoteGlEJeWQt1vXoB2HyiLxLD/0UeMmAparnJQcFzlcoyTB3zGgv76CpI2Fux/gvEJkYxmxkLpR1rXJMPd3ZtKr7/8xO8+9xXcHP0aLyNvZUTfD37ISr2MtQENdagpt8LmjKkGp+2VchqpgR1rC5tb42boNXkREQxy5xiUT6mB8xReX1TI/PGupMmD1tTibIW1yMmjZiJb//4kIUSugo9Gpvo5UIxriCIvbeGUnNkJmXgh6TPWNLyy0+832XfOTic+w0uFHI4NFavowN5QZOg1BpZaTU0tG9d2py2rgQjn3iMne4mUtVLoCMSQ1dZB8Tnwte9Y4IZdejmTVmMycMfZmsN1bWVsDS1QZ8eAdBQ14Cfd3+cDjnC/FoS8yOhoa4J/14DMX7ItBapuuHxuY3Hoa9Z2enjnzfPH++9sxfVKfmQONcLrg5Pj0JMzHoElx+DvdKVmVzTlF2GKBG14moMfvG1Nl6UtJZBayNnQ4+zLicJZEP7jWbPQyy+fjfywOldEOSAjzCIiaJhwim2osEQgKz8DPy4/jO8MP+tRrGQvHpeW/QR/rf2E5yvPsS8VWh6j4RCKsRIAJRBylY5PATfNt1TKgbzkc06vFQ0kQk0+RlOGDqdHb8qyOtl/6l/mcjX2jybphhzxekY2W9Ci8d68uHnmTdMRPx56EAXOoIeK+xoFYXSj8lLpkE8JP/HjbtWsRWXHt16d/Rt5HA4HA7ngUBTR6ex7mywTWmOrOQ/n2dt1RYqXQF5YtMpJDUDmnlqjU3TexUKcPvh3XUIjw5BTkEmdLQkrH5r2K6wsbDHgdM7EBETxjwJnW3dMGvwPAzoPbTLNnoG+g7H5v3rkLstGHaP12/NGA1wh8lQL0SeCkGBkANL2LHhQtoAyUcmS1T2cu3V5r7iU6Jx7MJ+pGUls1Xhvr0GYFi/sWzV+nokpcchMuESs6yh5i5NVWYgqXHCjw7g2zUf4oMXvoOlmXXj7Z6Z8wpWbPgCV+IvMIsasvCh6UCFoGSX0/YKQbUtSxxuBdWqtGESjVA2fVgrqoGZiQVGDqxfRW4NbZ84WrsgLTcOpoJVi+Y11bcpiIGhnjG83X1bvM8Lpj2DDTtXIgepMBBMWM1NoqI2dNEPIxptj4qFfFxJuYD1O3/D4kdeuu7rxuE8iHChkMMB0HP4EES9dwY1aQXQcWwZzKColaHkWBR8R957ndSboZ+TPUKow4hyKl0RXFaJ1NjUzt+RSX0oSDadErOazjf2hP9EzxZXDS2jU9Nj5NU1ngMAAQAASURBVBnKUeMAWFvXwssyG7asCCrs8EM/Nj8Af/8dioQ3/oLlrIHMc1JZJ4fx4O7I3xPGOqEkvjEEERw9vWDRo/54G6iuqcD7P3yCzLxUZqpMq7lpRan4Me5z9HTvwzrVmhrXTqIOvnwaVkoHyCFHKE6yzqo3AlgHljrbNHlHyXqrt/yAZ2a/3Hi7bk6e+P6dtQiJOIP41BhWsPbo5gOvbr2RV5CF3IIs/LbpO1bouQrejQUtFVGUMFeDJnFVqS3HrOELMGn4zGuajI8eNAlHzu1FjVDFhD4y+aYCME0cB319A0wcNr3FbagT++pTH+JkyGH8sW0Fez1JJPTFYJiKmgRJKvK6Cb3YKs2BUzu5UMjhcDgcTivcA/pCU6LNEnHtFw5rc3n+gXBoG+jB1Y9/hnYGasKSl50qqNai062E7GUozXf75o2QFVfAfGIfaJrowcDPGWWXkpFXkcEEQoJ2RaiBPm7wlBZCJdV2Ww6sw94T26Ar1oex0gLVqMG/2X9h/8kdeGPxx3CyUx3m0UDIlbNM6DNX2rDwEZpmdIIHC/NThwar9xJrIvHRT6/i45e+bxRTabWXar2kjHhW09ZIa2BlZsM2VKhZXFVdidWbf0BCyRX4Kge3CGihBncq4hrrbSlq4OfVHwtnPNcYONMaet7zpi7G5yvfwSWcgqPQHfowZonKaYhnE59Lp77eYhOnYRuHVqff/f5FVFaVMaHQCg7wQt8WYiMlIzsLnjh/6SRmjX+8y5OiOZz7AS4UcjjUhRozHId//xOJH26H0yuToOdpyz6kanNKkP7TYSir6jB07iw8aJBYCCcgxDQDNUXKTkh0XQOtCVhbV8LWyAC2elXwNXYF5B1PLNPV1cSuPU/jhZfX4fC6U8j4/Tg7XyxWhyPcWXFEQh0VR+RfcjnyHOwvhgHjmzx4Dp5cg4KC3PqVYRjVpxALYIbSVxIusKKNVq2vBRVU1EFNY4WSEn4YBq3/EorpsV3RgwmQ5y+dYIWhc7NCj0TIwL4j2ak5eg7d4erQHWWVpfhn7x8oFufDQmlL5RUKxNkoUxZh9MBJ8Ormw1aFu7t4X1fQJCh9zsjAhBWdGTWJ7Dz6u9Dbox8en/YM88ppDV1OBSxBrxFNFjYXCZtfj4JSriZeZte/l7w3ORwOh8O51Wjr6WLInFk49scGaBjrwmKCL7PFoaY1NTjzd4ViwtJF0LyFWy6cW8OUkY9AT6KPncc3IeZIZOP5xiILdEfvxvVcEgpDlSdZfUnN6AaCIk4zkbAbesFB2a2xhiJrnwjpeZYS/N1ba65Z61XXVjN/a9r8IGGSBDQbUdMmjxXsYSyYI7jmCPaf2tGivqXHc3Pozk7NoZqRWPb4W/j0lzcQJD3Maj3yEaygxWRRFpvanDr6ERaC52Tn2mJluD2obn3z6U+wYecqROScbzpGUxu8OOltNhWqCn1dQ+a5bQdXtiHjBm+Vdjrkt5igvIK4lCgE+Ay+7vFwOA8aXCjkcGiFQ0sLz/z6P6x+4TXEvrwBOjYmEGtroiolDzoG+njqhy9h6fzghjA0CIa3m9jqGPS38ka5LJiJhLryw3DU7dmp+zAykuDjHwPx9IcvY8nUD1GdVIc+yqGN3iU0MUfQCoah2AxJR04A4x9m55UUZiM1Mwo94A99kVGL+6XpQgfBHSeDDmLGmLms29oelqbWKC0oYMbY1LVtEAmbQx3PFHEMzoYdayEUXg9aJXa0dcXB0zsRnXCFdWzJd2Xs4Ofh49EXnYW8JR8aMQvjBk9FUnosZDIZbK0crlvUqYnrizDKQBa341FD0No0eSByOBwOh8Npy7glT6CmvALnVu5A7t/noGVlhNrsEsirpRj62CMY+cS8O32InBuAhDaaeCNvxG2HNmLfiW3oi2Ewgll9E7oZ9ko3XIoOZnY3DSvFB0/tZLUnNbqbQ76LXkp/XKg8xCYG2/OhJmgKsFI4xjZZqIFNISOtoRrVWumIMyFHMXfyUx1u6tK68KfLV+DQ2d3Mqie7JhXmxhaY1X8BWzGmELzOQmLhJ8t/REZOKopKC2CgZ8Rq5Gsdk/i/y6geZX9upyZt8FPkNSmHoxouFHI4/2FiY41Xt6xD3PkQxJwLglIuh8N8T/QeMxKaOrfOC4ZzezA2N0RtuRQmgpXKlGBCX2GI8tz/vANpVTo1mv2khLZg4SibPqQOKYl9FAxCndcUWQxSMhPh5dbWR6YBKpA27FrJ/p+8AlVB3U6JoIfS8pJOPzcyuu7m6AGRSMw8ILsC6kh7qvDGafcYuvVmhZtYELNpyzpB2mL1pIFCUTYzyW4o8iqrK1i4zZW4S1AqFXB17M58edrzUWyACsayilK2LtJeijOHw+FwOPcaYjU1zHhzOQu0C9t/GOWFRTAcaY6+k8bC1NbmTh8e5yahdVkNNXXoqOnCSKm6fjGAMdu8KCkrYkKhtK4WyZkJsIcrwnGWreCqQQOWsIUtXKAr0oehyATRiVeuKRQO7jsS2w5uYB59utBvV3DThSHSpPGQK+SdqisptGXW+AWYNmYO22bpqs0R8i9v7mF+LdTVNeDh7I2c1Cy2AUR+3XZom9ZNvt0kF1LdSVANSrUoiZxlFSXsuZB3t5ebzzWfB6VIU2ghPS5NTlLDncO5H+BCIYfTDPrH3TOwPztxuob8tHREnzoPmVQK626u7LVtHRZyu9AzkaCmoH1D7hpRFbSNmlZrayrLmPkymSabwxbakKAMhYhEEEt9c0EPdr1Gn8N2GN5/HEIiziI+Nboxqa01FPZRLa6A8X8rHB2BuqCnLx7BoTO7kZmXxs7r7tQDE4ZPRx+vANwqkjMScDHyHGpqa2BtYYvAPiNY+Ip/z0G4dCWYXScGYegpBEAsqg97oYKXVkBoqnLu4CfYeYlpsfj69/dRK62BiWDJur4Jybuw78R2Zi49yK+tL2hCagy27F+H2JSoFkLpoxMXXtebpwE6luikK8z7kdZwqMM+xH/0dcVJDofD4XBuF+aO9hj3zJN3+jDuG6jWuBh5HkUl+dDXM0S/XoPYmuqdQE/XAFJlLUsO1hC1XRVu8JjW1zVoFLEICh6hxGAL2DGvvxTEsvP6CINpZLHRBqY9yD7msSmLsW7Hr1BAwWpPVWu5VSiDRFu3jQfg9WrDvSe2IiwqiN2vsb4pRgwcj3FDpt7QNGFHoIlL2sTJL8qFREeXpVQ72Dhj4vAZLAFZAn0k4SqMBDPoiQyanp9QgWRxNHy7+8PCxAq1dbX43x8fIzopAgZiY0iUesgUZ+B8+En49eiPpY+9zoTA5pA3I9WjJCzWyaXsPFMjC+YFPnLA+A6LpIUl+TgVcgS5hVmQaEvQr1fgdcVJDud2wIVCDofT5SgVCkQcOYFd3/2M8oJC5reiLlZHnVIKQzMzzPvyA7j08bntx9V3mg92fXEY1UIlJKKWBsqVArkU5sJ/+GT2Zyq24iPOMnHQD0Mbk9KIQiEHETgPGWTQIL8VW9drPi51Y19b/BE++fUNZGSkwEHoBi1RfbJhAzlIQ42yuo0XYXvQ8a3e8j3Ohh2HhciW+czQmkVeWgb+t/ZjPDLh8WsGl9wI1DX9aeOXuBIXBm2xDlt3qVSWYfO+P/H49Gfx1MMv4JvyD5ggWoAcnMV+WAkOzIeREu8oIW/s4ClMUKRJQhIJNaU68BOGN65jKwQ5YnEZKzd/x0RIF/umFZuYpCv4avV7kCj10QP9WDechNf0pAR89PNreOuZz9t457SGCrvv137ChEZdsQG0BG2EIQi7jm3Gw+PmY/KI+rVzDofD4XA49z5ZeRlYv/NXxCZFMQGLth1kgox5300aPgMzxj5220WZgF6B+Hv376yB6oyWQSp0jBmiRHR37MGm2ogTwYfYz54IgKXIvvG6UqEW4TiDyziHWmU1PFzqG9jXgtaf62RS5m9NtSdtyTRHKtQgR5yGEf7jOvy6XI65iO///JR5VFO4HvkgllQUYOfhfxB+NQRvLvmUhd91JUfO7cFfu9ewelhPbMCOe8/xrazGXDJ7OWZPeoI9R2pCB+MIzAVbttVDIijVpJamNnjy4efZff25/WfEJ0fXB/EJlmwVXFAK7HqUlr15/5+Y28yvkerhz355Ezn5WWxNvCGcMLs0Det2/IKi0nxWh1+PfSf/xeb9a1mdTB7fUlEtjgcdhLuTF5YvfA+6EtVhLxzO7YALhRzOXYBcJmMrz2X5BdAzNYHnoADmm3gvUlcrxZqX3kBCUCgTCD3hx/z31AQ1lKMECcVXsPLZl/HixlWwdmu7CnAr6T/DF+c2RCA85wzcFD3ZVCDNA9L6QYJaJKwcneE4eBC7bl5CDMpKctEHQ1qIhISZyBp2Qr1J8lC/Me2mtjVABdnFK+fhau+OnLxMhMlOwUXwgmmz1GNKcaN1keuJjg2EXDnHREJv9GOvb4O/ja3gzLqnVNT09vRnnjFdxc9/fYWY+Ej0RH8WnEIFJBWpifIolthspG+Mt5Z8zgrGo+f3IiM3DXk1GVBTU2PrxqMDJzHfRLodrRtTd7+5SEioidThJfihXFTEJiWfmfMKO58KwTVbf4K+0hi+QmDjpCKt51gKdrikOI1123/Bxy/9cM3n8MvGr5GUGo/eCISp0pIdC4mT1JUn43BaY1Y1ycjhcDgczoNAVlwC0q/GIK2kDHY2PtBFxzcd7jZIdFm7/Sf2/zZwggu8WAOY0nAzlAmsSUhTc1NHz76tx0UBIDRpt//Uv1AKCtjBjdVC5UIJkkVXWWN16fjXGqcJD53ezfwEm4uEBN2GaqYQHIe2pg769x5yzcelWiopPY5thNhaOiI27xJqhKrG1GNqmJNftkQiYR7YHYHWon/96xuYCBboKfRvnFCksBA7wRmXss6w17kjwllHCY44g/U7V8Iebkxo1VRqMYGVUpwvRQVjzdYVrH708fDH4bO7ERUfjvLKIpQJhcxz+9GAhcwrkvzFab37QvgpuAk9WwTxUX1INkOOgjuOXziI6c38yA+f24OsvHT0FYa38DA3gSX0BAMWOhPoNxK2li3fr+ZcuHwKm/b9wTwnneEFdZE6Eydp8+ZqeghWbPgCbzz9SZe9ZhzOXSsUfvHFF3jzzTexbNkyfP/997frYTmcu56wA0ew85sVqCouIaM6aiVCx9AAk154GgOmP4R7jd3f/YTEsHD2/yRi0bRbAwYiY/RWDkKw/BiO/bERj3323m09Nm09bby+6SusWvYlIsOCWDFDSxqCoIR7X3/M/exdxJTXr3vkJ8ZAQ6wFY6XqEA9L2CEDiRgaMPqaj0mi2W9/f4uq2kpI1PShUCpQK9QgCiGN1yEflwmDprGJto5y9NxemIgs6kXCZlBhQyJkrjgNx4MOYP7UJegKUrOSEBEbCm/WzbZrU6TWiiqx8+gm9PLwY0l07aXRNUA+MLRurCrYhRVnSjtExIQ2nkdTinlF2Wy6s0EkbC4uOgkeiMg+j7TsZDjaqBag07NTcCU+jD0HM5FVi9tTKl4VyrH72BYM7DOMr3xwOPcxvCblcNpSlJWNjW9/jLSIKLbGCkFACCXdeg/AuD5zcK8RnxrDREI1HS2Y1JiyxnXDZztNFbrCm4lLe45vY9sO1wqluxWQcEYiJaULpyhioQY1tg5spGeCl2e9z4LpiOKyIhSXF6I3VG9MGIhMoCXowMezL7SuseJLvns//PkZEtJjoCXWhlhQY9Y5aYhDKmIbr+fp3IttiBgbmnZYtKuWVsEXQ9qsMdOxWQuOOBF0iAX/tV7fvRFI7Pz38N+sae8uNK3o0mOTmKoQFDgffooJeyTULZzx3DXvj6xo6PdAVbALQeenyGNYHdoQEnjiwkFYCvZtgg4JB3RDhjgRp0IOYc7kp9p9DruPbmHPgQTKhudAP01hie5KX0QmBiE1M7HDtjoczj0pFF68eBErV65Er14dN8bncB4ELh8+jr/e+ggmQzzhNHsGdBzNIM0qRs6WIGz9+Gvmj9d/ev0q7L1AVVk5Qnbvh8TFEorEMpgr2ppukyhjo3Biq8mzP3rrun6FQbnp8DIFwkuSEKgPpFVFdir5mK7fHBNrcwz78QX0TMiAEFvJPpRd/XrDyvW/1Yv/hMI2EXStaPAlXLPlJ8ybulhlmAl1bWkVw1gwhw8GsZVZKkbykYlYhMPaypatvNBtO1ugpmWnwEZwVnmYVCwZKc2RmpmM/OJc5gVIz5PMnTta+LUmNPI8KyxpkrA1dN82gguupoWwQpQ8cK4HdcivlY7MCub/PHkI8p8hDKH6+BvOp+u1JxSGx4QwLyALoe1zIGwEJ0QUnGf3YWlmfd3nwOFw7j14TcrhtKWiuAQ/PbkUdWoKuL0zHYYBbhDq5Cg6fhUpf5zEzqJCqKu/Aj+PeydM5eCZndCyMIQ0v4yJN6oagHR+mjye1QcDfYfddl/0h8fPx/ih09h6a3VtFSxNrdHTvQ/bxGhNe37YJDrRf5euBrNmJ61Ti8Utb0+hJF+teg/5+bnwwUCYKa3Z60GTi3EIR6VaOfN67tW9D6wtmprBHYGasPpqhtBR6qq8nNZyM2oSWW2VU5CFmtoqWFnYsS2bG2nK5hZmIzs/gz0PVbenScYE0RWEXb3QoanIhtRj2oJSRcP5DTUpXb+orACerRr1jdcXqUFPadRYt7YXyEfe4r0wQOVzoI0nTbEWS77mQiHnvhUKKysrMXfuXKxevRqffMLHZzl3N7RqkRhaPw3n2scHDt5et2yyiD5o9vzwK4wGuMPljSmNj6NtZwqnlyZAUCqx7+dV6Dt5HNQ1uibJ9laTdiUKcmkd9Ex0oSHUtfva0dqHQi6HTFp3TaHQQ+KJ2OoYXMk2hZ44BpkVpuijF4X48pxOHVd4FQmLl1Am9aB3md0vfOjU/m2s3L1wWbkZxchjK8KtofUGEnKL8wrx5ap38OLj7zDT6bCrQZDWSdnK76WoIEigi17CgMYuK/2kKUDq5F7JvQBDfaMb6mJrqmuy17o9ZKhDbkEmXv6cPFWExscO8BnMuqudfUxaLdEQaak0vWbHg/pVeXruHYFS5ii4hNZ+STxuTaE4pzGJjmhY765FFTOnbg0lUze/nirqZHUs8bo9gVID9YbiMnn7ryuHw7l34TUp514S7iKPn0Z1WRlLOvYePviWWtKc/Wc7KsvL4b1qETTN/vuMVVeDxaQ+0LI1RvxbmxCcHnZPJbpeiQ+H3iAXSI+VsbpTFeQXLRLEiEvLgY5u+8JOA71czVFbV8PWfFWJeTeCnkSfpRG3B1mimBtZIr808z/LnJaUoRh1qIWmXAtbD65nItojExfi/KWTzCuP7l9PYoD03BT4YwRLR26AJuJ6C4EIEg4zEW/s4M5vMmloaDLPRxIsVdX9VI8S7/+4nL12DdhZOjKPQLKm6Ww9SmhB9fQk1ZTUFK6V1l/verjYd2M/yY/QCm1XhQuQxWrfBmsg+jtA739tnepwRHodpOIaSK5Rj8pk9a8JrXurgh5PXaQJmVzWoefA4dyTQuFzzz2HiRMnYtSoUdctyqRSKTs1UF5efqsPj8NhlOblY93r7yEt4ipbUaDPOXm1FPbenljw5UcwsWkrFN0sSWGXUZKdC/fn2q450p+tH+6PqGO/Iz7oIrwGD8S9gFJR35XTsjRCIVLbFYEoNkRiYABNnWunoMWcDcKxtX8h+dJl9mc9C0tYjhgCm+HDIOpkgWZjaoB+Tu17hbTG3LU7zKycEJN7CX2EIY3hJ1QAkKch+QqSaTMFiBgIpvhx/ReQK+pYyAcJahWKPez63dFbpbhGxZ6OWBdBEWc6XSQRfXsNwLngk3BR9oBaq1XcWqEaRciDeo06PNGHrUnTceYKGQiNuMAS1ij4ozNpdjaW9qhSlDNBrrVnI1GCfFY4ke9ORxgeMI6lG1NwCa0uN/87kC4kolQowsLAZxvP83bvDYmWLtKlifCAb5v7S0cCDPWM0d25fSNvB2tn1CirWACKnqht0mEhcqGloQ0znn7M4dyXdLQm5fUo505BTeR9K1bi9F9b2P+r62pDVl7NLGlmvvESfMeN6vLHpLomeM9+GA1yh4ZpW3HDoLcTJK6WSCgNhYGdH+4V5FBCw0jSKKZRzdYa8gQUoESpsymu2tXXsKqoKS1C3LE9+OGPUCgUddBU10Jg3+GYPGIWzIwtbunzIGFq7NAp2LhrFYwFC7YK21AzUb0Xg1BoQYelIDvBA+cunWCee9TMptA28iCUKeuYp3NzkbABaqBaKR0QFH4Kj09/ptPH18crgHnykb+hGazb/G5RvUzrL+Z1tsxTkI61FIVIzo/GZ7+9hfeWft1hf27C3MSS1a/FinwYqPDPpHBCel1sLVVP/LWGrufp0hPJqVdhpDRtUeNWCeVIFcfCr8cAJtg2MMB3KC5cPA1HZXf2+jWHPAYp6G/ANfwiTY3NWc1cVJfHMqxbQ49brahgdSuHc18KhZs2bcKlS5fYmkdH+Pzzz/Hhhx/eykPicNpQW1WNnxe/gMraSnT7YCYM+7oyb5ay0CRk/HqUXfbKP39AR79rkqfKC4twZPU6BO/ez/4c/+5mGPXvBpvZg6Dr1iRIatnWf/hVknfhPYJDDw+I1dSgJtFiImEq4uCKlsJNlVDB0tSGzJh1zc70mX+2YcdXP8BQbAYP9IE61FGQn4OkTdtQfT4OASOfbLNa0ZrigjQUZMeiQlOB7N5OyPLtCVszww4JhlSE9R02Awc3fYfzOARzwZp1pEtRhAqUMPHNHq4IRX0xJiiU6IWBMG+2zhGMo6wgau/+6TJKTrsRxgROxumQI4gUBcFT6NOYokzFRaQoGCIB8BUGtygKHeAGA8EIoWknERZ1gU0XdpT+PkPw9+41SJBdgbcQ0ELYo8fMEqVgeL+x0NSon8rrSKG36JEXsWrz/1hwCXkS0roxTRKSSDh+yFT4evZrvL6mhhamjn4Uf+9dw4JxyPyZUpcp5Y5+z3KRjifGLb1mh9+vRwAMdI0QX30FPsLAFgIrFZZZ4mQM6zcW2tfw+OFwOPcmnalJeT3KuVOQSHhy/T+wmRsIi8l+UNfXQW1WMbLWn8aGtz6Clq6ky5rHJERe2LYLp//ZivK8AiCvANUJObCc6g+zsU3eb/RT284E4qIaGPe6d1JYzb26oyIyE/reDkiJjoWZ0qqFqENWMEmIgsTEBG5T+0GspromLc/KRvDXn0GolsFB6cqScyvlZaxZS0F17y79Gtbmqi1NGiirLMXFK+dQXlkGM2Nzlszbmc2O0QMnYduBjYiuC2WNUbK0IWGQpuAoYZjSeq/gPGvaEvaCGxMNNZSazLMv5Br1KEGXNZ/26wzU7O7u1AMx6WHooewHY5iz3xm5IGPheiUogAXs0F3Uu/E25MNnJJjiovIEth3ciFeefL/Dj0ebIwE+QxB6+TwslfbQEem2eE8TRZHQ1zFkNV9HeXr2cnz802u4UH6YeQ/qQp81lfNEmbAytcHj05sa18SEodOYGHtZfg7dlL2YCEvr33nIRLzoMtwdvdCjW9PzbQ3VtEMDxuDY2f2wauV1SO9XvOgK9HUM0LfnvTEowrk/uWVCYUZGBjOJPnLkCLS1O/ali4ylly9f3qKDa2/f8Qkgzt2PTCpFQsgl1FRUwNTOFo49b261V1pTg7B9h5nXX01VFSwdHTBgxkNw6dNU4FyPi3sOoDgzh61baP8nzhFG/dygbW+KqMWrEbJrH4Y+9ghultL8Avy48BlUVVXCfJof9LrbQJpbivx94Yh5eQPcP54Fg171ZrrVifUrEEaW9850k4G5GXxGD0PUnvOwmOSLlD2XUIUK2ApO0IAW6zayAsfOGiMen9vu/RRlZmPn1z+yzqO7sum9pJVd+kCNSD8P04pEllimitLyYvy48XMkpMRAXaINkVgE2fkaFNjYQe+NZcB/QiEVyQnBoUi9cpUJnO79+0LQbSqCaf2b1nbJ8LoQOahGJetIu5C/C6wb/WJqUIXeGMRMiRugQpJWWalrSqlprZEJdUxMvF5x2R42FvZ4aeG7+HH95zhbtx+GIlPWFS8TillyGnWdVXWOjURmrIg7ffFop4RCKmifmrUMv/z1FS6KT8BW6czWPqgAzBalwtzMEtNGd87snFKebSzsWLoxBZeQ/wutG9MkIYmErf8OU0IgrWHsOPIP0hWJ0BJpsdRlDXVNzJ2wiE0pXgsy0V467zV8vfp9BAtHYKN0ghYTfwuRJ0pnvjwzxz7WqefA4XDufjpbk/J69MEgMyYOeanp0NLRQbcAP/bzRmFpsmGXcWH7buSnp0NHTw++Y0fCb/yY625PNF83PrVxC6znBMJmTmDj+VSburw+BfKyGuz/eTU8A1X7mnX2eLd89CVCdh+AyWAPuM6ZylxKik5FI/WHA6hOzofDM6PZ49B1a5Ly0aN3305tZtxp9J+Yh9VLX4HFlL4oSsxFSN1xOCrdoQ8jVs+lIx6V4jI8+eGX8HRVHWRBrPjgc6jXiOCnHM1CUAjWLBbcEFZ7Cr9v+QHvPveVyttSnbn90EbsO7WdVYwaBhLUlVZi3a6VmDvxSYwYML7xuuRpFxp1HrXSGlib28HPe0Bj85Ua63KFDDZwhhx1bGqNGuhu6MnSnGnVVl8wQgFy2LF1EzV5sFJTlMLjyDKHhDRVWy6lokJYmt6Y/yT9jix7/G18t+ZDXMo4zaYYtQRtlItK2NAANdNpu6U1tHFkp3TFldhQJqAa6LXd9GgP8lOMT7mKi2XHYaN0hhFM2cZLljiFvbcvzX6nU8EplIT88Us/4Oj5fTgTchTpVdkwNjDFwwHzMHLAhDairpW5LUskXrHhS1wsPc48vEngI3G0d3d/LJnz8nXX9CncJS4pCqE5J9l3GyOQ+FvNhimkolq8PPd9aHRB+AuHc9cJhWFhYcjPz0efPk3/MCgUCpw+fRo//fQTW+loPfmhpaXFTpz7Dyoyzm7ajkOr1qK6tGmFx8LFCbPefoUJe52lODsXvyxZhuKsHBj2cYGGgx5iIsNx6cARDJg5BTPferlDhdSlg0eZaXNzkbABbWtjNu0XdvBIlwiFu7/9CdXSaniueBxa5gaN55uP90X8e1uQ8u1e9Pqjfuw/55/zMLK2hJt/2zXLu5npbyxH3tNpyNl3GRI3KxTnFCG/KpNdRsWCz9gRmP76i5AYtPWZa4AKbfLmcBO827yH5iIbJtIdObdPpVBYJ5Pi89Vvo0hWBrf3Z8LI35WlSVdEZiD958M4/v4n6L91PapKy7D2pbdRkJEBLTUdtpp74OfVMHPvBu/Pv2X3pa1b/x4ZwwzOorbrwTRJR5AI2trHsD7gwxmZSGI/9UQGLf4+JCMaNPZ3LV+a69Gzex/8+O46nA07zoJTaMKSVnT/2rkahtXtrwBLBH2UlhV3+vECfAJZIbfr6CZcTQyrvy8tXYwKmIApIx+FrqTzkwYu9u54Zs4rHbouvaYPjZzFCuuQK+dYcAqFs/TrOQgSHdUm2q3xdO2FD174DvtObkdwxFkolHK2sjx5wCw2xXi7Uw85HM6tp7M1Ka9H72+y4hKw6aMvkRUd13ielp4uhs97FKOemt9pHz4Sg7Z8/BVCdu6DxMEMEi9bVBaUYusn3+DEun/wzMrvYWx1/aYveRIKghKWk9uKKtTwtJjaF4kfbkNBWgYsnDq2VtkeMWcvIGTXfji/PAlmo5oC4ihcL3/fJaT9dAjGg7rDwMcRxaeiUZNZhH7vT8S9hOegAIxetIBt8VCNr1AqEZNTX7sQ5g72eOzdD+HWt/06OzcpBSkRkeiJ/o0iYQP0ZxelJyJTg5GVl65y1XXn0U3YfXwLbOYMguWUvlA3kEBaUI6cv89h7b8/Q0tLm00Xrt32E85eOsFEPU2RJmqU1dDV0ceiWcvg592f3ZeBriFE5SL0Eg1o8zhUV1JjXgkF7NB2jdcWzshAIhNHadKwOaVCIfKRjbkDVSf0dgTy6KbJyquJEWxykqYTqaFdWlGCoODTbLJRFXowYE33iqrOCYVGBsZ4/4VvsfPIPzhz8RhSZbHsO0Yv975s+8TNofsNPYdpo2ezU0dwdeiO795cjStxl5CalcREvd6e/h1eeaZ68+1nv8DB0ztx7Px+ZFWmQE2sBv9egzBp+Mx2g/k4nHteKBw5ciQiI1umjS5cuBAeHh54/fXXu8wAlnNvcGLd39j7w28wH98bLlP8oWlpiKqYLGT/dRa/PvMSnlu9Ak692vcWU/WB+MfyN1Etr0HPlYtYAEjD+QUHLuPCil0sxXbwozOue1/V5eXQcm6/gNOyMkRVajpuFlohvnLsFGyfGt5CJCTEmuqwf2I4opf9iezN51EZlYmKiFQs/OZTNul2L6FraIDn1/7CClBKQC7RrIGRqSm8AgdgyNxZHfJ7zElIgqHSRKW/IWEqWCIxr+W/Lw0EXT6D7NwM9PjlSUicm3w/DHo5oPsXsxH55G84/uffCN1zEOJKMfpiGAwV9b8/NDUYm3gZP7/0Aka++jGs7N2hr2uCtKp4GAqmLURL+l2jlVdal6WVDVWitBO6sw7uRRxnnWdataiDlE3gFQm5mP/Q09dNCKYvs5djLrKQFBJBHWycMdR/dOPtqNAYPWgSOzWw/+QOVFW376lVJS6Hk7Fq3xOa2MstyGJpytTRbu1j6Onak52qa6pYIUiFa2e6tl0BGXOP6H/t6cFrQa8hiZNPP/oSe760AnKrQos4HM6dh9eknAbyklNZuq+6lQGzm9H3cYSsuJJtdhz87Q/UVlbhoeXPdeo+yU+Qah6nFyfAbEyvxs+TmowiJLy7BWtfeQcvbVh13c+Z6rJytgVBYpIqtCwNG693s5zftgu63axhOtK7zWXmE3yRtysUOVuDUBaWjLx/Q9B7zAi4+rW/Snm3Mv7Zp9hxn9m8HelR0dA0N4Gjdw8MnDkFHgMDOlSPElS/qaKhSZyRk9pGIKI6ae+p7bB6uD9s5zX51dF3AMcXxkFeXoNtR/7C5eiLrPlJntY2giPUoM5Ev6TaKPy4/jO8teQzdHfxxmD/kdh3/F84C57Q/s9upoUvHsraDfkgX2YnwQOJiGK+jORzSI9DdS9Zx3Rz9LjuVgaRlZeB0xePsNReEtYG9RnOBDP63SaBvae7Lzs1cOjsbiZ61gnSNkIrQcdMtzXQa1q9bU5+cS57HWnijx6vOYZ6Rlgw7RnMmfwUExp1tCS3vdlLDXoSB+l0I2hr6WDq6NmYMupRVuOT2Hg9WyUO554XCvX19eHt3fLDR1dXF6ampm3O59zf0JoxFV+W0/vBYVHT9JSBrxP0etgh9uUN2P/TSjy76scO3yclE2fHJaL7F3MaRUKCPmwsJviiMioDJzduxqBZ067bGaY0uczYjHYvr4rNgZXtja2HNqcgPRNKhYKZQqtC190aYm0NZG84Uz9p+f2X8Brctmt4L0DrOyTSdkSoVYWGthbkYllDWK/KBDVaN1VFUMRpGPg4tRAJG+/XWBfGQz1xce8B1FXUYKByXIvChQJG1JUaCEs/hV0fv8JWeCUSQ+RVpSAKIXAWPKALA7bW0OCLJ4EeqlCusgiiVRATwQIF6tnIEaWyjifhYOWCuaPfgv91vEeKSwvx1er3kJWfDgOxMdQFDYReuYB/D/2NJ2YuxRB/1abmQ/uNxoadK1EhlLbwPWH3KeSz7vEQ/8UtzpfLZdh1bDOOntuHypoKdh75+Y0ZPBmThs1s80WaJvg6OsV3t0LFmJYmL8g4nPsdXpNyGji0ci1EBlro/uUcqOnWf2ar2Ziw+lTDUIJT6zZj8OyZMLbumO0L1XWn/t7KpvLMx7bcjtGxN4Xj82MR/85mpFyOhItv0zqoKkxtrSGrqEZtZlGL2raBqths5qFtbH3zAXs5ScnQH+CkUrxkwo2vEwr2hUMal4cRC+Zg3JIn79mGmntAX3a6ERqSpqnuVJVQS81fIrOgGuHxLVOTYxKDIauTsknC1tBraTm1L2Jf+wuFhbnwhB9sRU0NXF2RPnoI/RAsOoLvN3wOPT19FramqamJS7LTcFN6s+0amiDMQToSEQkJ9FGNChSjgP1/a8xghVTEQmZQi4jy8/WPo62H8QOmsim6a3lM09TsX7tX4/C5PdAS60BX0EetqJqt6vr16I9n576m8vYDeg/FP3v+QLoQz9akm0NruhniJPh69GsjAlKDfPuhv5Calcj+TOvSNHn56KSFbcJjSFxrHjRyL8J8y7k/NudBSz3mtERaXc1WHinxVVvv3v6S3VEijpyEQiaH9Yy2nTuapLOc5o/Er/egJDevQ6sZREJIGDRN9KHfS/V4t8kwLySc2MpShU3tru250X/aZPz58tsoDUpga8bNoUCTiqh0TP3iiXY70yRa0nSZc++esO3e8vbN0fzP+0ZeWsUkqdYoqqUQZAoMXzAHk5YtuWcLsq7Ae1ggm75UlU5L/iq54nTm3aKKyppKaDq2LDiao2VhiOKqalgrHdoIexS0EqV2ESKIodHNCBpm+qi4XD9NWqyehzx5k6BMKw6EVFzLgkOShCh4CH1avG8kyJGYaCQxYVNrxoYmGOQ3gnnzXW+CRalU4Ovf30dxYRH8MQKGgkmjt2GCcAWrt/zAiiUvt7ZfPEhAPBl0COH5Z+Cs9GQm0uRfmIsMpIpi4enck6XUNX+sH9Z/hiuxl2ArOMMDfvWmzFUZ2H5wI+uUPzf3tZv+nSQxkrwRj184gLyiHNb97e87BGMHP8S6xRwOh8O5PZDAVVZQyBomBuYtJ+bv5/A6ttnx5LBGkbA5FB6Su/kCwvYfwqgn53foPgszslCWmw/3F0aovNzA1xkahrqsbr2eUOg9fDBLN87acIZ5EtK6cQPyihrkbQ9m67SGFm1FEZqEjDp1lk0bmlhbwSOwP9Q1NK5Zk8rLqB5VjaykGmYOdlj+1+835d14r+PWzw+aWtrIkia3EbqIbKRATUMLlX3dcVWrZWpyelYlRGpq0DRVbbWjaVFf36qJNGAtOLSpda+KLqJaqISOhQWUPa1Qnl+O6qxqaIg0cAUXWlyf6lYSCfUkBkirjYO50gZaoibhSS7IEYfLLK2Z0nYpTZcm/8YNnQaj62y2EPtP/ctEQnf4MF9BEu4EZX14B01Ert/xG56a9UKb29E68bQxs7Ht4AZWvzakHpegEKmiGMjV6jBz/LwWtzkffhK//v0tTETm6IUB0KaUZKEIV6IuISYpEh8u+65LkqbjU6Jx8MwuRCdEsO9x7i5eGBs4hVn4cDic2ywUnjx5Eg8qVEgc+u0PXD5ynIlmIjUxvIcGYuySJ2DTreOR8PcilPJLXVoNE9X+ZTqO9QJBRVFxh4VCKnDpNWyvsBVpqDVe73p4Dx2EHsMCEf3pDlhM7sP8WahjW3ImFvm7Q+EZ2B8+o4a1WSP+671PEXcumB0HXV+QK+Dcpxce+/Q9lc/DupsLTOxsULA/XOVUYeGRK+zn4Nkz7suCPTM2HuGHjqGmvD7Ixn/SWBZ+ogqf0cNx6Ne1uJIXBG9FPxiI6osYSriNE12GFLUYP3SqyttamVojL+Yq+9BX9TpWRmexn1R4NIeuH6F2HiJLHfT8YiFbDVHKFJCVVqEsOAFpPx9mibi2Vg4oLM5j3jFGhsaswxkaeR5rtq1AtaiC+RGSZ2E+MpGDNDYUqVahCQ1BB9klWfg9+QdExF5k3dfWa73NuRwbisy8NPhjeItQEppS9BT8UCkux74T21QKhdSVfPOZz/Dnv78wr5g44TI7nx4v0G8kHpuyqIVQSSsv1L1tHchCoSeUrBcccYaJmze6WkHQSsU3v3+A2OQo9hi0ik2ry5T4RunNbyz5FE629/e/hRwO5+7hQa1JqQY9sf5vnNnyLyryi9h5lq5OGD5/Nvwnj78v648GqsvKWF3YUHe2Rk1HkzUTywvqX5eO0FBnkiCkEhFdJu5QPUrTazPfXI4Nb37IgkvIk5DWjWmSkERCVMjw0PKlbWqXY39sxJE16yGrqYVYSwNKqQx6psaY8fpLrJ5SRe9Rw3H0z42we3IEq9GbU1dcibKgBEx4btF9KRLSwEbo3oONQTZU4zv26qHyd19bV4Ihjz3MXmNtQcLCREgkIyGPRMJUxMNryhSY+6sQb+UOiNmvQFV8Dtsaak1lTL1/NwWzidHy94e2VvKRBde3prGwGXqf5WXVsMzpj5Qvd8Malhg7aDIKS/JZ41pXog/vbr3ZpseHK17BxepjsFW6sDTeKlQyX2wKQdFTGEKtUAu1kOJA7i6cCzuBN5Z8BlvL9kNqyKJl34ntzPvQQdQ0EEGvlxXsWW1+JvQoZo57DEYGbf2xHxoxi63Y7jyyCVnVKY3nu9h2w8IZz8Hequk7UW1dLdZu+5ndbw/Bv/E9MYAJSzgOrTmBTfvWYuljr+NmoEnIdTt+hZ7YEJZKEmlFSI5LxJcx72DmuHmYMvLmfek5nHsdPlF4G8hLScOKJ56FQkME6/mDIXGxRG1GERL3XkLc40vwzG/fw7Fnx/357jVIDJKVVbPCQ1OFWFidVlB/PbO2axbt4ezTE8fX/oXKuGyWHNyakrNx0DMzgYlN2w/m1pAH4IKvPsKRVetwdusO5O24yM7XMdDHsMcexbglT7TwCaTk5l+fXY6ivBy4vDoZxoEerAgsCUpA1urj+HnRC3j579+ho9+yg0gr0GMWLcCm9z+H5poTsH5kANT1tKGUK1B84ioy/zjJinQjy5vvkt1N1NVKsfHtjxB1/DQ0jfWgaaaP6n0HWXAITU4Om/eoymJ5ycr/YdWzryAk4xh0RYYs3a1cKGbd0GWPvdWuye/wgLG48NspFB2NhNnoliJa+ZU05rdDQm5ZXnGL1WZKZK5WVMDzlfkQa6gj7bcjKDp0BYrauvpjMtFDeNxFVtS0XmenUBUqjnYf24KrafW/P2RITV1TX2EwdGkFhGodAazwC40MYtedPqb9lODw6BDoiw2ZN2JrqHCiicjI+HA2pafKI5B8/KiQKikrQlJGPCtsuzl5QF/XsIXXTHh0MJvwMxaZsTWW1tA0ooE4HieDD92UUEiG3tS97YMhTID8byATLkovXK47ix/+/BTfvrmae7NwOBzOLUIhl2PtK28j5nwwW5W1GjACyjo5io9fZbVJfmo6Jr2wBPcrukaGEKuroya1AIZ9nFVudtTmlf2fvbOAi/p+4/iH7u7uDgNQBLu7dbOdU6fTTd3fORfOzbk5dZtz6uzZ3d0JCIIgjXR395H3fz1fJA4OBQVFvfde93Iex/G7kN9zn+d5Ph8018TkB03dSSspIPdROPNCbkxRWDLKcwrZ1klLoKRkCWlpXP13NwsuYQgJsab16GWLoGHEm8575z+y79nN7H00x3WrqbHiMpFy2A0HvlmNuZISsO7V1Oakx4TReHjsNKJ+PAXDr4bXiaclMemI+/MKC5zrPubdCi9pCd4XruD0ur/YJoW0gTr7fPLg8AmYdXfE7I2/QEqu6eeUoQs/RVFuHrzOXkKsSFjdim9ZFQfO40dh4sqlfP3Eq/W0EbBnH5IPPoTpTxMhLFp/m6riMqQe84SsgjqK87NYA7x2ApBEyCThaGalpNTTApnX/ZF+2hulyTUCNtXSqbmJKCmXhblpffBNTjFduJg86ls88r2AZ1HeqKquqPs6rTdrc+vXzTncEgSUeOCv/9Zg4zc7mq2/YhLCmSWNNbrx/TqlLtOmS2C4L3o7DWrydfp5Q3qORn/nYaxZzOGUQENVm/lF11LKKcHjQHf4h3qzJrIJmgq39PzQNCM154tKaHqy+VDEF5GYFscmIGm60by6U93PMaq2RCzC2PSjpZEN84UUIOBDRiAUvgFO//YnuLJisP5rBkTlajpzVKCoDrFHxLfHceyn3/HN6YPvbReXOnXnNvyNtJOe0F/AewKhAjXjjDfMuju0SiCjgomm8xK23IDZrx/xdEPzvKOQdTMQQ+d/AhGxlr3FaT1j2KK5GDh3BtKiYtnapaaJMcQlm66m0FRcangUbLbOgbRJ/eSgsqsFpI3VEfLZbjw+dwV9ZzYVwLqNHs6mEamoy7zsByldFZRnFaA8rxidh/RnneQX+YNEePnA/+Y9cIqKWHFKa9Oqeq/vn9ienPxlPUI9PJmoqtzHmomqlUUcpBzzwMW/tkFORRkOwwc3+T5aGV9x9iAunLmIdPdACBdWoKuMLj4eOuaF3niWxnYsSdh901U2PajczwbCYsLI9YhAxiVfaNjboNfIoTj96x/M/FlZqOZ9l4tMiCvLQ1xTAWHLDqIqoxh61UZQhCo4KEFiXgzyq3NYwjA/b8BaM2MyVI6IC8Pf+9fCFp2Zz0xD1IV0oMs1xi33SxjVfxLzVuFHRQX54TTvFyMGcfY+rayqfGGYCKUCOyrwrmlTQbbz2F/wDfWCqJAYqrlV0IdZnXjXxKuoWhlpGSl4VagbfffRNehwjaEkxDvJQROS5tWd4ZN3lyXHvY4YKUCAAAECmsf3yg2EPnwE818mQ8GxfoJbuaclUk974e7eI+g0sB/0rFufGPouQAIc1aRhF5+wGlxUhtcTjAI8qssr4DhySIvvk+pH14ljcWf/YSg6mUDBob6JSRsJCVtvQtVAD+bOLT+3kT811bmUbkyrxORJyG/duLSwCLf2HmriAS5tqMYm0cgb8fKWnbDq2aPJZwxqzi/c/hf2LF2J4AV7IK2vRuOJKEnMgqK2Bubt+JsJqy/alHp8/jI7RklZWTa5aNHDqdWJ0W+SMI/HOP7T71Ad0gm6s/tCTFGarc/mPY5E7F9XcXDlany27c8m30ci4ORVK9B76iT4XL6OwsxsyKmpwGnkUBac2Bzs+35YgT1ffoPw5YfZhCilLxdHprGAmIrcUhjPnYfobdsRUxVSZ19DImR5NQdKva2QsOMWMi76Qk1IB6ZwZtOD6XlJSEcR7j45iYmf/dbktRWFDHpbLEaP8jIkRsbD/doWyBZJ8nggEpJC0rCsdoBPzl22xdLQkqYh5ZUVdXUnP8i7kVafyyvqRUl+UL3bMOikltuPruLYpb2oqCxnAYHi1GYX4l/nU01eVV3FwlReVSi88+ga81k0q64PHiLo/ykoJkM4Cbc8rgiEQgEfPAKhsJ2hE2j0k6cwXjG6TiSsRURSHDozeyP8u2OICwiCUecXe5e8bSiMw/PMBSSHR0JUTBw2fVzhMHwQK7xeBHXnKHWMRCHq1mqMbZh67IGy5FyMWPNLq46FTr5z/vwV/y5YiuA5O9jJlPzkKMSkwD8ONn1d0X/2tFY/Rppk07OxfOFtfK/ehEIXIx6RsBZJLSUoupjD58p1vkIhQcflOGIIfC5dR3ZKKuvadh026IUr6MX5Bdiz5BvEBwSzYo4ea5i3D0vvHbbwUwyaNwsdESok/a7dhsGiwVDpX3/CpUlKvbn9UZaah5t7DrDHz08oFxEVhW53JxgbOkMmrRpSycUvDdCg+5k76Utoq+vhuscFhF+vWbsVl5KGVd/hsP50PJyM9BFw8x4CfB9Bp9oI6tBBKUpAZoPJ+x+gOqME3ar7Q1qovrOsXW2EAHjg6KU96NGlT7MCH03spWYkMQFMlct/olUDukgsjUJyekKz67bUafV8+rDZpDiagFRVVG+1+TGtr2w+8CsiosNgAydocHXhjXtMDG2OMqFSKErzT6RrCRnZqSjmFMES/P1CabVaSkga0QnhAqFQgAABAtoJjzMXoOBozCMS1kLTaJkXfOF59iL0rL9GR6astBR+V28h+IEHKsvLoGNuxibk1AyaX5+sZchnnyBspifClx+B9vSeNanHucXIuOzLBBmq0Vq72UE1WGJYOJ79cIINAshY67ImcO6DZ5CSlsacnRtbLaCxcD5D/j7ctQTfd0cFpwya45tOepG/IXmAR/54EmnRsdAybbqFoWtlgR8un0TQ3YeI8Qtgk4umjl2YNdKLGu239x7E1W17ICYrCWkzLVREFrJJPYNONpj79/oXCoxvk1t7DkDORg+GXw6r83+kP5V6mIO7pBrhv55DYsizZj8HkCg4asnCVv1MSlZeuPNvXN22GzHrL/JMiNJ9JZAFTV42Ig8eQalQCXS5JqhCZd10J70nLdAFejCpa+ZSzUrBJCFpPnDLdofKCz4/lilLoPhUFkzQo9n6ixraYVGBzQqFuhr6bCslm5vG1o8bk4N05oPdcEKwpVDz/cC5f6EDYxjBCulIRDRCmKciBQo2prZWlZHib2fVEiJjQ6FcrcEeE79/dyrVWoiMC3vl+xcg4H1BIBS2MxnxNUEIVIjwozaMIz0mvkMLhR4nz+Hs+k0QlZGCrL0eqorLEfbbH7iyZScGz58Nh2GDIKvcvBkurZeSCHd95z6E3Aqqu17LwgSf7Pgb+jZWrT4mbXNTrDixHx6nzuPpzTsoKE6AuoEeRv32I7oM7s93DaAtoPUDcbPmRRNJbWUUhYW/8D5orWXAnOkt/pnU5UyJjYHFuinsvUQnMvKgST3piWv/7oGipgacRg1FRyPkgTsLrFEZ0HTlhh6D2rDOrIjNiI2HhjH/NOhXgdYnRvabiKG9xzLRLjQuA1JWJijTk0S5eBWbAJi7ZT1u7NgHz9MXkFAUWfON2UD23RAYV1vyiITsPoWEYc7tBM/Sm3gS/Iglub1IjKtdNeZHbRAKde+bo5fjQJy+fhgRlQE8Pi1EDjcd6UJJmOw6u9WTyOGxIQiJCkAnuEBNqEa40+TqMv8aWkOhDnNDyEg7C6kY3nUMXpXadRZK52vu+aoSqhasHQsQIEBAO0KrxWqT+K8P0rS/jK0u0mPj0JGhELkdn3/Fgljk7Q0gIiuJ2PMXcP/QcTiOHIo+0ye/MFiOxLfFe7bi5K8bEbX2bN31ZDcz4ovP0P+T1jeZqab4dNM6tnHy6MwFZF0PYYGFA2ZOheuksWxzoj0ozstjQwfNhWVIaivVefK96Nhp3ZkuLeHJlRtsK0Z7iiuz0CFPRDqHFwYmIGbdBRxYuRqf7/gbHQ2q3eP8g2C0fCRPSEwtJBaKK8oi8O6Dlw4MtBYTh8744r9tyElJY68FTYfW2i1RfnW3ZQsQ2MkaN7b/h8CoR3X/HkkklBaRh25VU5FXS8gAiULRqPLzxdhPpzT7s+/7Bjz/vxfXis1XozWbKSQiBocFQKVak2fajwJKooVDoKtmADOD1j1vtP5NdS5Z3FiiS00SNFcXkQhk/o9s06XhMXK5SBKKhrGO+WuFmZBHdznKmz8uVAnqUQECBEJh+1M7bVfRjD8fdTFrbtdxzYIjvJ7gzLq/IO9oDDE5KVRxKlCeXci+RsEUF/7Ygsubt8Np5DCMXbGE77ou4Tp5HLqPG4loX/+aQAsdbehaW7zWyjUJbjStSJc3BfkexkfGNPv1ksg0tibSliEgkV5PYPL9OJ4QFCrOdGb0RmlsJlt5oVWZtl5fLy0sxNMbd5GTkso6xJ0H9+cb1EKCuPf5q8hKTGITpF2GDIRpt64oL+VAVEoCIpL8p+/ElGqKDbpde0DhHXpahsgqlESpmDjKGv5sCQnmkThkwSds8peKqF1ffI2CjEwog38BIiMkzwqklPT69GN+WBjZoKK6nHVZVVgZyAulxElLykBbo/lpATkZecz7aAl2HP0TxUIF0KzWZyEpOULp7PttTDthSM9RaC1e/m6QFpaFanX9tCOZc1MP1w9usOR2qfEQZLppOiKE/aGqoAHXrvwN0VuChooWVBTVkZaXwPf5oOlIWrOxs6j32hEgQIAAAW0LBTdQPdoclbnFkJB7ubfz26KyvBw7v1iOMuFKqAy0Q3VpOcozC5jXM0EBFXTRtbHER6u+gY6FabON5qUHdiIlIoqJp5QAbOrUtdn6tSXQBgRti9DlTUG1ZlVpGUoTsyGl19TPuDgyteZ2LQwKfBkk1Nz+7zAT1WgjiseipJMB9L8YwsTXxNDwNl9fJ/udcE8fJvaR5mXWzYEJcI3r3rKSEvhevcW2ueh4jbt2Yq9JbZ0ppsh/K4WEOVEFqXarRwllbU124Yd9/96w69cL2Ukp7DHQNs79A8ehA6Nma3vlajWkRdaHg/Cjdxc73FFTQ0ZmEtT5bHUUcvNQzC1kdeuLmDluAdYkfw3vgjusHqWQlBIUIVU4HsLiQlg4dXmrP4PEJEYiOy8Djuhb973UrCabGhIL6fWjx08WOdS0jhYKQS43C3OH8gb6tJZOVo64nHIGldwKdt8NISueTOFk9LTmn2IuQMCHhEAobGcMO9lCRlkJGZf9YLR0eJOvZ1zxg5ikBCxdndFRubl7PxOlCp7EQMpQDWXpeRAWF4Ph0hFQ6mHGkmGz7wbD58h15KSlYf7WP5pdsaDOpUUrfFpeBVqNfnTqAlKioiEuJQn7fr3hMGIISy5rC8jcOWTZt8zTRLG7WZMEszzfGAxd/XppXA0hPyESaKkw44fqYHtE/nwaOcmpzNevLadIL27ahsqKCkiqK6I8t5AJwq6Tx2Ps8i/YxCadxK9v34tbuw9ATF4a0qaaqIgowOPzV2Ds0Il5KJbnFaEkNgPSRk3Ft4KncRARF4PKW/RZJMGQPjQQk1d9jT1frEA5j6RYTxW3ChUof+m6r5mhFQy0jBGRHoDO1XI83Vda3UgSisFIlwkQF2veg5CgqUUS2CjdmFKJyeBaXUkTU1znYJDLiBd6EzZHCacIElwpnoKOVpsduL0RAE/44WGN34yQEOsUG2mZ4ctZ30JK8tX//dDvgxF9x+Hg+Z2Q5yqz1ZXan09F6jPhpzDVtYSJHv/3OFFcUgQPv3tIy0qGlIQ0unXq2WygjQABAgQIaAr5D3pdvgKd6b0gIsMripUmZKEgIB7Df2p+Oult43/rPvJS0tn/V5VVMHGHhEKytFEf2ZU1H2myLeWwO7bOXYylB3c2Cf9oCJ37a8//7QHZxtBKbpi7F6ul9G0s4TJx7EtXiluKTW8XFqRCwSXG34zhmZSjILb0U49h4tilzWrD3NQ0ZMTEwXT6BL5fV3I2Y7VgqNujNhUKaXX6v/99j6z4REioyINbXc3qTm0LU8z567e64MK4gGDsWfoNSgoKIWelS6sgCNhwnwXDzPr9Z0jKyzJ7ooY+krWUZRYwf8YXeQ62N1QX1fqO05YNhaeUFzYvXJahDBIyUi+tv8xHDcXT/w5DlasJTaH69x5Z24QJ+0JFTq3ZteOGU4U/L9mEqw/O4v7jm8w+R0JMAq4O/TCi70Soq7R+QKKktGZYRhK89aUFOrMZRxILoxAMcVCjnwNpcRksmvg17C0c8Dr0cx6Kq/fPIajqMWy53ZhVEEHCYZiQLypQgUGuI184CUme2iGR/uxzkJmhNRxsndlwggAB7xOCd3Q7Q8LYoE9n4vzGzRBTloHmhO7MPJmm8jKvPmUBHwM+mc43ZasjUM7hICYgCBLqCjD9fhxLjE0+7AarLTOYIW8tWhOdWWJaxI8n8czjMTNifhvc2LkPN3b8BwlVech20kdlfg7OrN+E2/8dYunSr1qckWE0dakD7z4Ep6QEiprqiFp7DpoTutUEdAgLI8f9GdLPeLNkO/LcayuouBSWFGcFMT9o7aY2jflVyMvIxMPDJ5mvYkleARQ0VKFvbYXAOw+gNqILdKb2ZIm/VaXl7D3r8d855l0z5qvFeHzuMivWqLNM721aM6aTZoFfLKJ/O4+shGQIiYogdMl+yNroQmO0IxSdzVgxVJaWh9RTnuz5Ip/GjoCVqzPktbWRlBLD/AUbd0fTkMAKCUfbF7+/6fu+nPUdft2+Ep4FN6HG1WaFUIFQDgtN6WTuiHGDWvZhzNzQCuafrGKFSWVV1UvFxZehoaKNJ/BEFbcSIs/9X6hTGw5/FKOA/b0SFWwPRVpKDoN7jnqtFY9aBrqMRGpmMm55XEaCcBQUqpVQJsRhz4e2qh6WzPqu2W70fe+bLKGuqqoSssIKKOOW4uLdk6ywXTjta0i20qdRgAABAj5Eek+dCO+LVxH54ykYfDmE1W10ziZ/57i/rkJFXwedhzQN6+oo3N1/hIk/BouGsA2LoHk7oTO7D7Q/qk/1pRpDzk4foV/sYzXhzN9/eivHmhAShp2LlqOspBjyDsYQlhGD5+WrcDt2GuO/Wca2bF4Fer0o2M7z3CVkJ6eyFda0h2F1HuAS2kooDk9F+ikvlKfmY8zaX9vsMVWW14RViDQKgamF6lQRKfFXrkcplZven9SoTo2KgZiEOCx6dEfUEz8IKUvB6q8ZkLGsEdJIEI7ffB3/frYUy4/vQ3lJCXZ9sRxi+sqw3zydfW4hyrMKEfP7BebzLSopgbSz3sh1D2fWN1Tj0mcyblU1EnffYQ3wzi1cwX4Tnx8HzJmGq//sAodbCkkhXkGQRD6afOs/+OWr8uYjhiA/OA7B3m5IQBSUuGooBwcZQsmQlpTGV3PWsHXclyEvq4CPR3yCj4bPRnlFGcTFJF5rk6lWXMxDFjRR8/mMGuKUPJyGms0d8j4s43KYZY+jvQu6WPO3TmgNygqq+GrOKmza9wvcK69AmavB7j9HKAMQ5mLxtBXM55wfaZnJ+HPvGqRlJ0NGWI49/hvuF6Eop4xln6yCsV7ztgcCBLxrCITCN0CvKRPYCicJKhnnnkBCQ5F1QCtLy9Dzo/EYuvBTdFQCbt0DqqphtnoCKyij11+Acm8rHpGwFjLIljHRhPela29FKPS/dY+JhDozekHrI5c6YY0EqajVp9la6bdnj7Q4CbkWWkvZvmApCrKyWRdSVE8GpVll4FZWsdcz9YQnux1NhjqPHo6RSxdCVPzVxRwqAvPSMpgwRIbaOuamKMvMR0lcJkuya0yeTzQkZGWgrNP6jnF6bDy2zv2CnfCVB9lCVVsJJVHpCLrjwaYNtKf1hJhSjYhNxR+JgZRU7X7sDPrPmorb+w+z9wN51dRCJ01KNaZudql0GTQndoewpBjyHkUgas0ZKHQ3haS+KrKu+bPijNaa6TF3hNRvOgbbqRPx6I9/mHBmzLVm03ZUuGQgCRFCAehu1wuaavwnICnxODjSH5WVlWza7bevtjKR65HfPRSV5EJLVRtTnT9BN3vXVvuf0O3F28AzhRKbL9w+jlg8gylsUcIthA/uscQ6M9jXrZMkIgrFpQXYeeIv9v7o32PYaz+3M8cuYCvM9x/fQGpGMqSltPBxl1lwsnNtNhzGL/Qx9p76h61Hm8IG4tWSda9HYJgfS28mkVGAAAECBLwYsnyhVNd9y79nSbdSuirglleBk5EHLXMTfLrp99dav21POMUlzOZEc6Iz1Id3QcqJR2zbhRqQjaH6RW1UVwTuvc++r602Slp8rEXFrOYU0ZKD/apZrNlKUP2UuPcus/OhiTEKDmmtkHbk+1/gf/Mu29KQttBCeWrNxFlxcDIintTb4hh1tce4tb+ywJLXgabzyFdPTlkJSloakJSTQb5PNOSfe6w3pDQ+E5z0POhaNr8d8KLHdmDFjyygRbG7KfQXDERlIQcRNwNQVlAIo097Q5amBJ9Dq85maycjeN4uluZNHoQVFRWwWj2BNzxSCCjPKUQ1lwvZ7sZQM1BDaWw6kg+5sW0v9VFdkfMgjG2+oJqLjLh4GNhaoyPQfdwoPDh4EgEFHrCqcoC8UI3nZBE3H2HCfhCTkYTLxDHNPp9RPn7Iz8hEamUVnGd8BguT7njmdgOFhemQlJDC2M4fsek6BbnmPeabq+daG6THD6qlLY1sER8fzuxwRCCKIHghE6nQgSELbeGCy5r0JBy6+dxBemYKVi749bWn92zMOuPP7/biweObdZOBLsa90c95CBMS+VHKKcG6Hd+jvKgcTugHBa4Ka6oXIR9hRX5Yv+sHrFu+rdnvFyDgXUMgFL4B6BcqpazRGqbv1RvIS89kJ9yuwwexoq0jQ+sSMhbaTCSs9VSs/X9+j1PSUBUFmVl4G9w7eIylEWtP7clzvYSmIoxWjELI4v8Q/MAdnQb2bfF90ol295IVqJAA7PYugIRGTYeSBK708z5I3HMXA+fOhHl3R2aeXTsZSol8MX6BLI1P28y0RWsfdJLyOncJ9w4eZ+sVhIyKEmx6uUJCRhrRv52D6eqJkGog0hZHpSHrsh9cxo1udXFPfi+Hf/gFXFlR2KyfwePbQpOSYcsPI3HnbZisHMvzfWrDu7CpUs+zl5CTmALzz3rxfJ26t7F/XGaTlsb/G1kn2FLHn4qy+G03UOgfB9WhnSFnb4DoX86wdRGaLHwb0Gsc5uaJ1OhY9hwqmZhg4pL/4eyWv5HCjWO+hOVCHJRVl8LBugfzDWxMRWUFjl7cg3uPb6CquiatjjDVt8RnHy/DiL7j0VFQU9bAxKEzcOr6QSYSUoIcFWfdMKAuXZk8CrW4+ngKdxQgF0cv7YVL176ssHxdTPQt2KWlnL95HMpC6rDidmW/Y2j6kSYelaHBwmUoWCY5PRE6Gi9PuxQgQICADx1DexusunKKJd3GB4WwKSqLHt2Y51trk3nfJNG+T1FdUQm1IZ3Y38lrkWoyamLyg2rV6qoqlOTnv3GhkAI/SvMLYbd5ep1ISNDWhf6CQSgKSsSDIydbLRTSwEHAnfsw+XYslHpZ1jVYqRaMXHUS+mYmGLF4Pgu4U9PXrastaboxPz2ThQ6SJVJLXmfyxyZrmTB3T3CruRAWFWVJvVqmJoi78ASSespQHWRfdwy0KZWw4zZkVZRg24+3LnwZdIw0RRjywIMNJjS09dGc5IyYdecR/+9NNi3aUASksBYaUqBAQ6q7FXrwfp2I+/sqs0iy3TUPklr1ghg1uMP+dwhJ++6zIQCLeVMQv+kqfC5cfatCIU1SPnv0GFUVFdCztsSCnX9h75Jv4Z16BzIiCmzyragyDwrKqlj4zybm1d6YgNv3ce73zSjIrv88Jikrjx4Dp2LcsCXoYt52PuqvC/M+3Po1fCrvQaFaGZlI4QnbI1ShxaYgw+CH8LgQeAe6w6VLyz/LNYeCrCJGD5jMLi3B3fcOcguy4YKhzFKI7HlKUcxq6E5cF3iW3cCdR1cxadjM1z42AQI6AgKh8A1CKVf9Z7c+Te11oU4grc2mxyUwI2v7AX1YodCSCa4KThlE5etPuuT/Qh3D5k70nLgs6Bi3PsH4daGOcWJwGIy+GsH369ImGpA2UGfBLK0RCslnJTshGdZbPqkTCQkSv2i6jlYfQtw8WJgKSyKuqmJJug+PnUJZcUnd7c17OGHyDyuaNTEmLm36l6X2KfW0hOmsCajIK0bywYfwPn+ZVFgyaUPwvJ2QNtZgYl1xeApy7odBy9QYQxe2LMylMDsHDw6fhPelqyjKzgVEhPh6H0rqqkB7igsSd99l4nBt6AghpiDNfAU5hTWG6I19jjJv+ENITASGi4Y0WZcmD6Hs+yHseoMFg1BZWMqup/TCt0HUk6c4/P0aFGRkQUxBhpmCU0Hp0H8gxv6yGWkPvFCcmAQ9TVU4d+oNAx1jvu/7f49shF/IYxhzraANQ+bxl4U0RCeF4JdtK/DLss0dqsNIRRH5zZy9cRQFebmwgkOdSFiLsJAITLl28MFdVFVUwifoEXo5vtmVnJy8LMQmR8IO3VkwTDQ3hAmXBBXLVDySEbVPkAd0ND5+o8cmQIAAAe8qrU26bSuoRgp180S4lw+4VVXQt7NG58EDWtTorCyrSSkVlauZZCIBriw9n63cikg3/f7SuEwmbkkr1Ndubwp6fHL2+pBQk2/yNaoVlftaI+JYzTZKS6F1XrcTZ6E+yoFtcjRExlQT+gsHIXrdeSYG1oqEVPOe3fg3MmLi626rpKOF0UsWotOg5gPKYv2DsGPhMoiqy0F/4WCIayog5agHE/LA5bKaNG7TVSTtvQf1MY7sMWXfCERVfinmbdnI3l8taVbTZxN6TCnPItnkGL2mIrK8Qp+wqAhbNQ+YtQ1Zd4KhOZbX51xMTQ6lUYXsvSUqy2vjxEnJQf6TGBj9bySPSFhb5+rM6sOa1wZfDGWryhIGKsjPeDv1KE1uUj36zN2LJVkLi4miorAEqvq6mLlxDfLSM9iEIJ4HtJAYy+95Joskmsyk4BJLDIAsFFCCQsQVPcO98zsg3ucTdDHn7zH5NqDAwdVf/IETV/cjIOwJFKHKIxLWQlsl8YhEJcpx3+tmmwiFrcU7wIMF8glDGCFcH6QjEdWoZl+TgTxkuQosMFAgFAp4XxAIhe85ZKJ8et1fbI2VhLKK/BI8OHwCZt0dMHvj2pd6I5JRcPiRJ8yfjrq21D1MPvQQ2h+7sJNsQ+hkXBydhm5LvsKbhltdk3gn9IK1YmExEVRX1U97tYSIx76Q1lNlRRg/lPtZI2b9RXaCl1GQx77/fY+Qh4/YRJ7qkE4QlZVkKxoJRzywZc7nWHZ4N/OTaQiJMOSjQyKh3vwB0BzXjZkqh365j4lyRl+NhEJXI7bKm3UrCEn/3UP81utQ0FDD0Pmz0WvqJCYAvwxa8d0yZxGKi4ugMsAGqoZqrJDOuh3EPAQtN0znEUMVupmy7jCtYygo1Zs7U+eajkXfzgri0pLsdZd97hlDZuhUyNFKSGMBsRYlFwsmgLLbJ2azP+VVmq6ytzcUerNr8XJIW2nDetUo9hpTR5yCefx33UVGVi5GjvsfpJKLX9h9jYx/xqbabNEdmkL1U220MqFYrQKv0tu4+uAcpo+eh44EiX4kXv6+6/u6lOPGKAgpQ5grDCEhYWTn8W8QtCec8hohuRiFiEEoKyDt4AwpyCAf2UhAJAuYIUFRgAABAgR0XDITkrBnyQpkxiWylWcSQh6dvoCLf/+LOX/8ysSPF1EbOkI1h0o/G6j0t2W1RPpFX1aTNqSquAyZl/xYY/xNTxPWbp1Qw7Q56GskbLWGlMholOYXwKgf/3RaRRdzNrEY8fgJayDTVNnBb1Yzf2jz3z6GjIkGShNzkHbaiwlJM9atRpehTf0o4wKD2dq0uKEKLH+fyta7o9adR0lUGnTn9IPaEHvmUUihILRVk3LEnSU+k+hMljQtCQMhkfDYj7/C98pNKDqZMJGztsZ9tuIwC3+kzxu1kIAoY67FjqFxo7Y4OAlm5rbM8ifkiQ+bfqRQF3oPpB5/xG7XXBgg1aPxW66jOCIV4qryKEvMhVyPNz9NSO8F8ldMjY2F8TejoeRqCSFRYRSFJSNxx23s/Pwr/O/YfywZ+YX3U12NS39ugyo0Ycd1rhsIIbHQhlvj7ef2+DQ+HjamQwVv6Goa4H9zVmPRT9OhVNz81poSV5WtJWfnvvl6lCgtK4UIxPAE91GFShjDBsogv8cyJCGaDQhIFr3+5o0AAR2FjvNbQkCbE+bxGMd/+p0JVrqz+0JMUZqdQCmtN/avqzi4cjXzqnkRzuNHsSAQKgaoo6c2vDOybgXi2TdHWYCFkos581zJvhPMuo00OWfp+uLkrPZAUlYWqgZ6yPOMgErfpid58iksik6DwVTbVt0vdbwpjKM5ar9Gt7u5+wDrtlJnkvxzaqFiR76LEUI+34v7B49j9FeLeIJK/vvqO9ZBFFeTr/PaSTvzmHmlWKyfxsRCggyXqZNKHeqotWcxfe2PMHGgZLCWcWLNepQJVcJm+6cQV60PD6HJyGcrjiDun2uw+LV+Kqu6tKZ73/DxV1dWIfnAA8irq8KuX29EP/HH4/PX2DpI5pWnyLzuD4gIQ1JTsdnjqH6eVEgFXtopL9bdNnwDa8cUhBHz+DEivO/iek4O8xASVZGF2U8TWSFMiEiKsdeOphNi1l9ARrdoGODFKxoevnchLSwLjep675xaxIUkoVWtDzef2x1OKCRqk4zLUAppNG0akFk265ZyuZCXbf41bS+UFdUgJiKO+KoIJrySSFhb+JKXogZXD964g5jESLxtKL3Pw+8uAp75Mo9KE31z5v3TFkEwAgQIEPAuQ2uhNKHGEa6A1d+zIGuhXTfxRYEUJExRIEVt4is/1Az0WJM78ZA75DoZsFpIc3x3VpNU5hVDbWRXiCvJoiAwHqmH3YHicmb78zYwsLfBsz0+bGui8SoskecZyW7TWvGRIAGJHxSqRwJZjfdzGg7/sAYyVjqwWDelbrtDzkYastYT2OTh+b+2wn5AXx7fbhouOP7z78x3zfCTsaw2KgpPQe7DMBh9PQqq/etraGpgy2yYhtCFe9G5Zy98tHplix8LCYR0MV45Bip96mt2ej3jtlxD3OZrLKyG6uJaSPhrXI9n3w5CSXwmeqwcDXFpKeZVSCF5tHZONRzVmwStrIugafO6+nk4Cz0/uR7PwEnNgePIoXgTJIaGw/34GUQ/DUQFh4PCrGxW8zf0fpSz1oX5rx8heN5utqo+7usvX3if8YEhyE5JgQP6NNkao78bcC3wmHMbwRFP0dmKdzKzI0DBKmXFNQ1ifnBQysJNFOTefD1K6Grq43GKO9toIbseWj+uRYWrybzNk8tjmF+5nMybn2RuKBiT76K7713kFeSwDSIaDrA27dQh/OAFvDt0XDMSAa/NrT0HIGejB8MvhzGRkKAigjprBkuGIvyRNxJDnr3wPpQ0NTDxu/+xtNuwpQeQfTu4RgQTqvH9eDr5bwRM34rUIx7oNmIo5vy17q143NAvvt5TJiLX/Rly3MJ4vkZdSio6pOTl0JVP9/RFkIBVHJsOTlLN5Ftjcj3CWUpgWSmHebmIKkrX+ec0hIQ5lUF2eHzhChPIarl34BhbUZEyVGNrKrXFHE21qQ7uVCcSNu4aS2krMw+clkIG4JGPfaE9sxePSMiOTU0e2jN6saRiTnJO3fU0aUjHUxyejMKQJPb3Z18dQqF/AlwnjsXuL1fA6/xltq5LE4m0cqz7aT/2OAoC4plXYWNIqM6+FwIZCy1E/3oOeV6RGL3083Z7z+QX5TH/usLiPFw5sh7u+7aiJL8C4uoW4BQVQW1klzqRsCHKvSwhpiyHqOCXrwYVFOVBqlq22ZOvNORQwilGVSunB94EhjomUFFUZ8ElDd+XtVCHlAoiEWERFsDypqE0Y3MjK9a5NUFTuwRalzaEBRJSY5BXULOS/DaIS4rC8t/n4dD5XUh6loiMqAxcu3ce/1s3F25P7ry14xIgQICAjoDf1VvITUuH6c8T60RCQlJbGaY/TQRXXJilAb+MyatWQKxSiIlTiXvvQcpQFbJ2+sz/mEIt/Cb+xULTlKWVsWj3VmgYGeBt4DxuJIQhhLh/rjM7k4akX/JFYVACen08sVX3qWlqzLY4qO7kR75vDNuKMOpih/1f/4CqsgpmIdPYAobOo+TPV5iZzerPWrISk3Hilw2QtdZjNb6cbc2GRM69EIhRDdtA0KuFtmZUhtrD7/rtVk1Iup88y7wFG98nfUbRnzcAQuKiNY3nBpsspfFZKM8qYJOM9Fhj/7qM2E1XYT+wL/Mf37P0G/bYKKQk6ufTkLXVY6vFtCZNwwz8oOvpZ9GkYuzGy7Dp17Pd/LLLSzksQJBEXPcTZ7Fp+jwEPngMEWVTlFcIQ8pInW9ADAnNKgNt4Xf91kt/RmFObt0aLD9qr6e6tSPi3KU30oWSWJO6MeRPnY00VKAcPZ3eTjJ1b8eBzCNbF8Y8IiFB7z1jWLOa2d33Ht4WFD74538/Y8OeHxEU8BTZ0TkI8PfD77t+wKb9a5mnugABLUUwUfieUpSTizj/IBgtH8lOvI0hsVBcURaBdx9Az8byhffVY/xoqOrqsLCQZztvs+kidWMDdJk0mfmg0Lg/rYzIKr2dDk8tlPwVGxCEp7+dR6adH+S6GqEyvwQ590MhVF6FuZs3QFyqdSld5Gd4cdM2xP19jRW4NNVXS87DMNZlHbv8S3idvcieZylD9SZFWS3SxupIL/BGZXk5xCQkWIgGFUuqg+yYqFaRXeP5R93gqkIOJHX5r+PSyUhcWwnFuS0/0SeHRbA/FbqZ8P26YrealZ6S6HQW/kKiYPqFJ9A0MkTKAbe6AtC0W1eo2jni2r97IGuuzYRHVoTdD0VJZCqbEKwqr2TrLzT1aPrjBIg/N/KmAjZx121wErPZhaYSZ67/uVWekS0lKiEcp68fYh01gk7cdJz6/adCxdwRlaVFyAh4wDxp+EGvoYSqPCqz81/6s6hTFyIcyDp4wkJNX3tKQ5OTVoCIyOsnFrc1JNBOGDINu05sQgQCYMS1ZFOQldxKJCMGMSDRnYsxAz+CrDSvwPymMNa3QHhUKGSE+P98BagwkTMrLwOK8q1L7msLKAVvw+4fIcwRhSuGQVKoRtyn5zAC/th98m9oqenA1ODFv2f5QY8rMi4MiWlxEBeTgL2lAzPfFiBAgIB3Cao15TsZMmGwMWRrQzYuFNIxbkXTsLCGUADgV4f34O6Bo/C5dBVphcUs7M157CgY2FtDTFwc6kaG0LGoqWneFnIqypj+22q2+hs8ZweU+lpDRFoc+V5RKIpIQe+pk9hadGugFeruY0bi0dmLkO9qDDmb+i2G8swCJO28A10bC4hLSSEptEZMpGRkflBzmsjPrF/h9DxzgW1TqA3thKKQRFTkFkFcRY5ZFpG/X3O1raSOMvMzL+eUtXjNm2pS3fn9+X6NjkHOTg/FUens7+SLHrPuAqSVFFAVl4fwb4+x6xW11NFn2iR4X7yGSuFqJqapaSii6Fkych6EoiQqHQVPYiCmIstW1CX1VZk4STU0nVvzH0exTShuZRXSTz2G89iRGLv8izafuCotLGKDBN7nr7DJ2hqEIK9nAeNhc9kkaFRuOkS0aiZG+SGuoYDSgprPCC/zwieKkMcC3xpD1xNKHcgzuyEDeozALY8r8Ct9CCuuA7O/odcqF5kIhS+EIAxNVW24dn3z/oSEhlpNk0MB/J8/al7LCisgIzsVb4vD53chJCKABcJQkjR7v1dzWUhMQJg3jl3ei5ljF7zSfefkZ7FpVNqaMdQ1gZGumWBC8T1HIBS+p9AJm2iYZNsQOuGLKkixDldLoDQ8upC4RaIRCV0dDUrum7Z2FWz79ITHqfNIvfCUGWQ7DxuGnh9PqDN3roUeByWLJYVFQERMDNa9ejBfl4bQ45zz52/YuXg5gj/ZAaU+VmxqsPBpPApDEtFl2EC4Th6HnYv+B3F1eZQl59R40/ApqEoTsiEhK8OEVaIgK5t1dDWd+6MirwRxf19hHn9S+qoQVZSp8WLhM51I98+Jy4TiwJavHYs8/5lVJeU8YmfDlY7alWcypy7LyEe3McNZ956CYijJWlpBHrmpadg8cwG0p/dk6dK1JwjN8d2QcsyDFWPG34yBhJYiIn88icBZ29jatbCUOPKfRIPLqUS30cPQecgA9n4iX5u2JiQqAH/sWQ1prhys4cj87AqRh3huJFLcz0NGTQ8SCqoQkZBi/i9KrhZ8nw8qThXtnF6aDtfLcSBueVxGKuKhA15vHg63BGnCCRjSfTQ6KrSOUFJahGOX9yGpOhoSXCmUg8NWjinQZNzgKRgz4CMeYez+4xu473OLrTQoyCmht+MAtmYrI/Viz9NXgXwUq1HF0uXEhJqmW1JqMyEjyf93XXvj4XcPRSWFPCIhISokygrdAqEcXH94AYtntE4ojE+OwfajfyA5I4EJ3WT0TpOdfbsPYWvsoqIvN4sXIECAgI4ACSRUOzUH1ar5nJbVoySG0AomiTokUIlJSnTID6vkJ/fV0T1sUjL0gSezmtG3tkTPRV/CupdLk2POiEtgk3H0mLTNTdhtGtdII774DEnhEXj29WHm7SdtrsWsdXLdnkFOSQkz1/3MgkhqIR/ohqnLtVCzlpBXqffMjg8OhXwXQ2YlQ5sWNKWpO6tPzWvjG8tshqgJ3BgS5CTlZVvViKdAPKpHm6OqqAyc1FyEfXUIRWFJUNbTxsKDm6CooY7s5BQ28aisrYUNk2ZBWEMWtr9+VLfiTaF5VJOGLT9c48+49mNE/XKG1aRSxuqsxqamOD0H9Dz3nf4xs0yiEJi2hixuts5ZjKzYROhUGUMFGmwiLgkxyEl8hswgN6h36gMJeRXkPwts9vNDcVgylHVenlSsb2MFdQN9xCWEQ5GrxtO8JsEtFs8gK60IG9N6/8eOBDV7v1+4Dn/u/Rk++XchzpVktU8F6DOKECyNbLF45jeQEJese0yB4b646XEZ0YkRrEbqZOGAob3GQF/75V6ZrUVKQprVY7V1Z2OqudUo45ZCuh1q4ZZQUJSPh0/uwIhrxRMIQ79ryL7HkGuB+143MGHI9FbV62XlHOw/ux2P/O6xx1hbkxrqmGLh1P9BW73en13A+4VAKHxPkVdVZiduGtFXcGia1EphGSWJWS0yHW4IFS3tIe605YRUS5L8qCCiTm9uShrEFWVQVVaJK//sgGVPZ0z/9UdIy9dPL1FC9IqTB9j0X8DteyjhlEHT2BDjN8yD3YA+7GeSSEniHp3MKdVXdQDv6gIJgbS+3WPkiLrisHbCrLqsEip9rJB20hPhq05ASk+V+e2QF6TGuG6Q1OYtXjJvBKAsqwDdx/JPeOaHmVMXiEqII+tmIHSm9Wzy9azbgSxZTkdZB6r2Oug2dgQrOAh6Lmqfj8ubt0NSUwnaU+pFwlq0PnJhx0wemCbfjIH29F5I2nefCYQstbmay4RSs+6OsHRpHx9LCu3ZfWIz5KuV0ZnryoQugsI6tLgG8Km4hyT3szAdtRDKFt2Qee0xS5Bu/BynnHjEPG2szV++bmuka8rEQvcnd1DCLWTJbDWpx6mIE34GeTkFDO09Fh2ZIb3GwKVrPzx6eh/hsSGorqqGtak9M5j2C32MXSf+hoaKJrrYdMe/R/9AWlYKFIzsoGBoh7LcdJy6cRj3vG9i1cLfoSjftsE0jrY9cOj8DiRxY2AEXrGNisREoWjoaRhCU615b6v2JPCZL5SF1CGJph+CWXFWrYeAZ09adZ9pmcn4dftKiFVIogt6QRnqbN0luToG97xuoLikCIumr2jDRyFAgAAB7Ye2iTFS799rVggpfBoHTePW1aNsu6KVWyJvGm0zE3z04zcvvE1ZSQmOrf4NgbcfsLRbupTnFbGtC6pHTR3rPa/p8S7cvglPrtyE57mLyLkaBGlFBQyZO5tt1cgoKrD6lpDQUULqSU/I2enzbBbReZOul5SThUWPep86qmNJDKR1Yo1xTuw21DTOfRQBLqeCCYckwPEce2YBsm8GwmXM6FZZyFj37IGIO0HQmuTc5P1AvpUkDmqbmkBNVx+2M+ag04A+dU12dUP9ukTnrPhEWG6c3sQHUtpYg/lv05YLqqqhv2AQEwtLYzJYiB/Vo4SOlQXsB/VrUer2q0D2QpkxCXCs7gtZofoNFjWuNiIRiASvi1A06QQVq+7ICvFga+mNU51p7ZpslUYuXtCifxPjvlmCXYu/hj/cmSehHBRRjALEIRzZSMdIlwUQFu54Gy4NU5D/+m4P83t+GurNpthIiHKwdWYbFieu7IekhCScbF3wNMwH1x6eYwMA8tYuqKooh/czX9bA/Xzq1+jeqennnddBWkoGdhZdERsZDe1qwyZbRGlIQFk1B86de+FtEBYdhKrqSmiBv+UCXR9TFYrwmBB0tWnZ5zD6ffHPwXVsStGUawdtGEIEomwNPCo1GGu3rcTaZZuhrNgxp1QFvB4dV/ER8FrQCbX76BHwOHueJcM1XD+gQi1pz12WlMsv8exDSN7bsfAriOkqwmpFjak2eciQkXH0v7exZ8k3WLx3K0/Ro6SlgVFLFrILP6xcnRHx11Yo9jBj3o3lGQV1qcd5PtEsqViokot+s6bUfY+cqgo0TI2YRwp54lEqXegX+1DwNJZ5/WVe88ez5Yeg9bELWw2uKiljQl/GJV8WMqNjYdbixywlJweXCWOYLwqJYsp9rGtMr6u5bEUj9bgnen00gXXoG1NR9nw6VUIC8SFhkHcy5rvOTtdRJzrfOxpZd4KQ8O9NKPe1rknI1lNlE3qUQHfk+19qBN12eO+FRAYgOy8DTuhXJxLWQtNohlxLhCb7oKwgG5pdBqAgIYR1rDXHO0H++ap65lV/5D4Kx6SJn0FOpmUd5rmTvmAryDfdLiK+vGbNm7qfncwdMWfCondiXVRORh5Deo5mF045B9sOrcehCzshJSwDCUjBi/sQZ24ehYioGCwm/g+SSvVrLZoOgxB18V/8sX8DJgxd2ubH1tmmP54G34EQF9CFCUSFxFDKLUY0QpDDTUcv+8Xwj6xZU3rT5BYWQ5grwiYc+CECEVRUVuJpBG9a44u48eAguOVAV24v9lgJMYjDEJasw+4V8BCmxn2hrtrUz0hAx6TseYK3AAEfIj0mjmEJxyQ+kT9eQ0iIyvePw8hfV+FDgz6E7/96FaL9A2D01QhWm9HUHk28Je6+g91ffI0lB3cywbFhfU8eiHThh5lTV9b4lbPRR9atAET+fBraU12ZeEZ+26mnvJjv4PiVy+rEN4IauFe37UJFXjF0ZvRmE3c5d0Og1MuSrQPT8ZCIpza0MxPmqAmcdsILMrLy6D97aqsed98ZHyHwkweI3XSFiXhUKxN0fNG/noeCuhq+PLCjiRBMm0BUk9J6dWLoM/Z9NDXID8Xupkg57Ma2f8innFbcTVdNYBY8FNZHCcu+B28xq6a5m9e3+VQq2dF4nr4AzWp9HpGwzs+Oa40kxCIn3IfVUGp2vZC48zZ77cmSiARjarxnnPdlid8uk1rWcLbo0Q3zt27EufWb8TTere56ORUNDB24DObybz7VubWQkNnFuhu7EHe9rmPdju+Z9ZWckBLbeqFNHkKr2zBodh1U973aTsMQf+8odhz/E2aGlmwrpS0ZN2gK1kZ+g0AhTyacyQrJo4pbybaKIoWC0M2uJ/Q0DfE2oGGJ2rqTHyTw1YY7tkZ8pKlNWmVuOKWoCi3IVyvBi3ML193OY+qoua99/AI6HgKh8D1m8PzZiPDxxbP/HYbqYDvIdTZERXYhsq4FoCQuAzPX/dRiP5H3CUoeJuNs87UfQUSmposoLCYClb42bMWC/E8o6MWqp3OL79Np1FDc3HMAFTnFUBlgh5Tjj9gabh0kommq48bOfeg5eRx0rSxYoTBg1lQcXfUruz159FQWcaA3px/rhFK6XPyOW0jYeRsJ22tMjKn4G7JgDgZ+OrPVj3vk0oXIz8xCwIaLSDnoBgl9ZZQl5ICTlovOg/vziKBUvJI59cMjJ+sCb8jLktaQJTgvWBehpGRRYTZJqNzbCsYrRtcVXyRWU8IdCdWX/tmOToP6sQ52W5KWlQxhCEMe/KfalJ77ipTlZzF/GLPRXyD21kEk7X8A7LvPvqagqol5c7+Dq+tQBD6qFf1eXtRMGjoDo/pPQkRMCDMLprUHNeWmHjHvAntObEZwuD9LGVav1mGvYSW3AnF4hrjKcBRnJPAIhRIKatDsNhzx945B3VgKmpptu4Zg6/w9jh9Xwu07ZxHDDYO4sAQ4VSWQkpTB/Bk/wMVlMN4W0elOuJJyBJXVFXWiXkOyhdNgYmIFexfzFt0feb9s2ecDPa4Z3/vThD5ihEORWfIMA10+vEbPu0ppaTFw4G0fhQABbwdqbA7+7BPc3LkPRSFJUOlvw0Ik8h5FIOdBGOwH9X3pJsj7SFxAEKs3ScBSanCOkDbRgNnPkxCy8D/c+e8wZqxb3eL7lFdVgeOIIfC9cYttelA4HoUR1iEizBKCac2ZQv7IJ1pUTAzdx4zA7f8OIXrtOZh8N5bVc3QctCFCNSx5EaadfYzMK0/r7srYoRObeiRPxtZgYGeDab+swrHVvyLPIxyyNnpMvCsMTYKCpjo+2/Ynj0iYFh3Lngf/2/dQVV4BGWVFaJsas8AWbkUVey81hu6v1k+cJiVttnxSt4YtLCfFpiPJkzvslzOI9vXnmdxsC8qKS1CUl8cC1/hB53ea9uPk1/hE6riMhZiMIlLvXWVpzgSF13QfNZytnLdmepbEwm/OHUZCSBjyM7KQVF4BLQULyGaQQWQx3iX8w3yw78xW6MAYprBlTVP6jEITbcHwRmFSJDS6DKz7rCEkIgK93pMQEh/KbHLGD57WpsdDftPLPlmFncc2wavkJrOcoXXyKm4VXDr3xaeTmg5cvClow4nIRCq00LSRTD6F1NU2fH67lkDTmbLC8szvsDHkaU5CuJvPHYFQ+J4iEArfY6TkZPHFnq3sxO917hILpyAsXLph0Hc/sACSdw3qJhbnF7Cihh7fq/D05m2ojOhUJxI2RK6TAaQN1fH0xp0WC4WF2TnwOneZdUAp0aw4IgXS5tqs80VrDmSULGOuBTEjdQR4uDNDYzrpD5gzHQ4jhiAzPoklVLMVCSEhqA6teV2ooDH9bhzKc4pQGpuBkrhMNglq27fXK6UE03NG4SHx0ybD+9I15o8o72oLp9HDYGBnzdNNvfT3v0xQVehqDMOlw9l1uQ/CUJiRhWK3fOh/NqjJ80dFJYW7yDsYM78cTVopadShpb9rTuyOsGUHEfM0sM0LMxKOyFuvHGWQQNOiqgw1Uz0iz/1NKjnFqCjIgomlHSZ/+iXEJCSRkSkEZyO9V07ppcCJd5n0rBQ8DnSDFRygIaTLU9Sawg7F3CKkP7kFZXNHntdX0cgOCfeO4Za3B8w7tf3ahYnreGja9UVMmDc4pUWQV9KAsVU3iIhL4HF8Mt4W8iaOqMYhhMMf1lze5ySZG4McbgYcO09t8TGWlRajorIcMuD/+41WXSQhg9jU1Lf6uAW0jnIOf08jAQI+FIYumAMNQ33cPXgMMRsvseuUdbUx+qtF6PXxhDZvHL4JKKiCvAdlFORf6fif3rgLSQ1Fto3RGPIJVB3WCQEH72Pqmu8hIvbyj2xVFZUIuu/GPLDFxCXYFoe4mjxkbPVQlpSNyrwSFqqi0MsCaYnpOPLdGrifOIP5W/5ga8vz/9mI3V+ugP/MbWxl1+CLoXWrwbQmrDHGEcXhKSycLu6vKzCwtWa+ga9C12EDYerUBY/PXWbTgSLKYrD+aCY6De7Pswoc6x+IHZ//DyLyktCc6gIJDQUUhSYj+kYgEwlz3J+xxnpjaGKQ0prznkSzCT1+Xo20BSSlq4Inl2+0eT1K3pnURC6r5j9NTmJXmRAHsmI1j5VbVYGSzES2nPDpPxsgp6wENUP9Vx7ooFqEXh+CE5cIoXR6HZsPTOmoXLh1HEpCarDkdqkXA+mzErRYzRWY4omSjATIaNSv21KNL6tjhsj4mkGHtqaTpSP+WbUfviGPkZKRyGp/B9seUFd5uY9ke0IWPDamnRETEwLFahWeZGZKjY4VDkMXC6dWDTFQQrZktUyzE7fSkENCaSR7P3dEr1gBr4dAKHzPkZSVwcgvF2DY53NRkl8AMUnJd3KKkFYNyOvD48x5FGbUmDAbdbHHgE+msxCSlkK/yDiUktdc2q2QEMTU5Vjx1xJi/AKwe8k3qKgoZ+sMSnqWbPW2JDyFJZlRYWL280S28sF+flU1ko+44cqWnWyVgMTIYYvmotOgvjj1659IiopoEjZCqcHsoirHhEJO8at3A+nxkeciXZoj6slTJhLqfTaQxytFbUgnJB16yArPyLVnYLJiDMSUak5CtKpChT8Vj9WVNSPtkrr1JtkNkdSruZ7WPdqazlZOEBURQ1JVNExg0+TrCYiCsLAociKeIM33JgoSn0FDRx/zv/4F8koqiIsMhf+je8gOlYeFZWdwuW/2VyS9P+nyKkJwW+EX8hgiQqLQ5PJfa9WFEZ4WuoOTmw4p5fqiiPt8lcHQWAOdOrfXSqw+XHq3PMTnzaAPUXyP/X+vRYFwDtSrdCEKUWQJpyGXm4k+w8Zj3NSJLS6gqBlyVFIahZw8aKCpYE1d6xKhAhibG7Xj8yygrSktebemOAQIaA/IcoQuJQWFrFaQUVJ8Jz9chrp54s6+w4h9Gsj+LqeuAtcJY5m9TGvC/koLC5mQx8/OhaCQPHqeqAZ+mVBIGx+0qkzHJGetC1kXE4hEZ6A4MhXVBRwmaOrNHwCNsU51zzlN8EX9eAqn1/2JGb+tZrXhdxePw+3oKdzctR/iKrziGq1Fk+chQeE0BVk5eB1o+nHQvFkvPB8e+m4NJE3VYbZmElvHJWgDSH20A0I+38tsbiidWc5evybhtaoaGZd9mWe2hLYSKrIKm61H6fbk5UgN/7aGmvO2/Xoi+v5T6FWZNLHDoYk4DrcI4kW5iL9/HIXxIaiuKGMNffpcQ8fke+UG+7dCad+2/Xq1m5fii1ZZhYSE39q/UQrniEoMhw268T0GNWhDXEgK+XFBPEJhbU0qItx+HqYUKNfWHohtwfyPluKXbSvglX8LGlw9yEIeRchHulASVBTVMGfi4lbdn4qiOsKFQ5lVFb/XgMIileRU3snf4wJejkAo/ECgAJLWrgZ0FKhAImPe2IAgqAy0hYZjX7aiS359e75cgQnffsWSh1sC/SJT0tFiRslqw5oKDuRVWBqZDtXhL58Ko8nGPUu/gYSxKqy/HwtR+RoBllYcEvfcZV6C+gsG1omE7OeLCDPvl0K/ONw7fJylrBVmZ0NeTRX9Z03Bvv99z/xJaN2jMQUB8ezPCM8nMO7cfollHifPQVpfjXWOG6MzvRd73ml1KGDm1pqCUUgIhYEJEBITgbCUGPI9o9htS2PTIWvV1DumJDrjeXKhWpsfu6y0HIb2HoMr985AhCtS52dXzuWwtLcMJEFZTR8i+clQl1fEqIUr0L3PYBQV5GH9ivlMKKTbPxECWyXVUDWE/vwfoar0ah3zlpKcnogr98/gcaA7yss5UFPRwgDnoRjkOhLiz7vNb4qyijKW2Nucx4kY6rvfDcmJ9GXdc3ObjibktT/d+wyBpo4+bl84geAnnqiqqoCBqRUmjVyCLj36tqqAoqkUl4Ej4H71EnSrTXiSlIkkRKG8ugwuA1oeZiRAgAABHYmGgXHvGlQjnVn3F+Rs9GC4bATzySO/PrKfifTxw2fb/uDx/nsRKro6CLj3kG1kkIdeY4rCkiGtpMBWhV/G6V//QGJ4OCw3TKsT84h83xjmU0ibLZrjeMNISFDUntkL/rvuoM/0j1Cck8cESfp/t+NnUBCYAMXuTacdqTlMicqRJb7Mi6+9mpthHl7IS02H9bez60TCWmgSUHOiM/O9DF95FFJG6pDQVEBxRBqzWZIyVGPe2LRqXRLD38OYREVOXBaUejVtLLcFAz+dgZAHjxAg5AlzbifICMmx1NgMJCNc+CkUVNQgKcqBcFU57CeOYj6EytqauLhpGx4eOcVCV0SFxVFexYGUrBwmfv9Vu3vLk0f1bY/LuON1DVk56ZCQkIKzfU+M6DsBWur8/SDbi/KKGo90cfD/90S1laiQOKoreevRiuICFCZHwHbkp/jQoFCRNUv/xu1HV/DQ+xZii5KhKKuEcc5TMLDHCMhIt24br0+3QbjjeQUpiIMOeAOnaEoxXSgRo5wntfGjENBREAiFAjo8FMAR4x8Ii3VTIGdbP2FDqwTk3Xduw2bY9O0JxRYKTy7jR+Pajr3QGOPURJBLP++D8twidG/GJLoh3heuoJzDgeW3Y+pEwtqOK5kzF/jFIvteCJRcLZqc2CSN1BB96yk2Tav3dKBgE0l5OSYymq2ZzHwTaynPLkTa6ccQ11TEozPnMXDuDNatbA+SIyIh52DIV9yg65R7WiLPJwrqI7oygZBWrHVn92GG18Hzd0GuiyFKItOQfMQD5j9P4km0o6Is9bgHVA10+U41VlVWMqHkdTpT5BVIHoE33S8ycVBSWAql1cWsKzrlo8VQtHDlmcTilBbjr++/QHFWATrDFSpcTYBb0+0Nzw7Ar/+uxK9fbWFpZ+3Bs5hgbNizGsIS0lC27wMxGQUUp8bg5PVD8An2xMr5a9law5uCTJgpta0AuZAXahrmQql5QkIiEJOr79AXJkUg7cl1dOszGArKH2byGQmDn/7vpza5ryETZsDv0X345j2AQbUFVKDBPHCSEYtkxGDgmClQ13qzBbsAAQIEfOjkpWfg7Ia/oT7KAfoLB9XVKuQvSMGB5HHtfvws+s78uEX3R76At3bvZ2KX7qw+PF8jIS7nVhD6fDzppUJcXkYmnt68wyYGG4qEhIKDMbSn9UTKEXdUFpY2SQiW72zAarPNM+azqSFCUk6GpVDHX/GD2mB7SBnU19d0G/KhpinIgoysVnt6t4aUiGiIKchAxqypP1rt8556zAN68weyycmqwlIouZhBbUhnpJ3zBic9j4mh2beDmfe3pBZvTUP+jXSbbmNH8N/wIBH0NVbiyY987j+/4/DKn+FZcAPSInKo4JajoqoMlt27Y8b6n5rYKJH1z4ODJ2AEa+jBBGLV4ihBEaKLQ9h0pYSMNKx7uaA9KOWUYN2u7xGfEgtFky7Qs+uDiqI8eIV5wzPADd/M+wXmhlZ4UyjKKUFaUgbZnHSooOlaL4XalXDzoaxQ38yvKC1E3K0DkJKQRi/HD8/3tDackEJX6NIWvod9nAbhoc9tFHHzWeqxKMSQRfEtwuFQUVJjIYgC3k8EQqGAZsmIS0Co2yNUlldAx9IcFj2c3spKpMep81DuY8UjEhJUoFFhlX0riHmcDPnskxbdX88pE5ghcviKo1Af4wCF7jWJwtk3A5F9PxT9Z0+Dpglv14QfEY+fMFGMAlAaQwUUmXWnnfNp8rU87yhk3QiEnL0etCa7QMpAlXkQpp70QllRETj+hQhdsg/qIx0goaWI4mcpbDoRwsIw+HwAon4+g8SQMBi101Qhrc2UF3Ka/ToVmsJioqwz3bA7nXzEHdVlldAY7QAhCCHylzOIWHUCWpN6sMdY8jz1uDAoEfO2bKwrsMlT59Hp83A/dQ6ZsQmsm23TxxX9Zk2t81dpDTTVNn30PAzrPRZe/m4oLM5n4/YycpaQkpRlRs4BSKi7fdDj68jOSEMPDIa0UH3BRv4n0lw5eOXfxMMntzG01xi0NZWVFdhy6HdIqunBeNhcCIvVdE1VrZyhauOK6Ms7cO7mUUwZOQft2T32CfRAZk46xETFISMtwwqzcM5TdOX2gUiDdZkSbiHiEQEutwqxV3ZAQkkLZXlpKM5IhJltF0z57H/tdpwfEgpKKlixfgeO7fgTQb6e9JGFXS8rq4DxEz7HoLGtS5gUIECAgHcZTlExAu8+YMEM5N1mP7DvW5lKfHz+CoTFxVhztHFDk2pU5d6WcD99rsVCoZKWBoZ9Pg9Xt+5iKcNqQzuxxjNNAWacfwJlTU1WC70MCuMgsU+lH//JOFrVTd7/AEWhSTwTglTPRf58BqIK0tCZ3hMK3agepkTgQMSffwIutxqhXx2E+vCukO9iyCYJM68+ZVsl5F+dccYbwffd200oFJMQRxWnnG3qUBO+MXT8BB2b5rh6q5zS+CwWkiNtpsnCWEKXHcCzrw+zxG16/FTzk4dh2pnHcBg5BPo2VjyWQuSj+czdi60+a5mboOdH45mo+yqiIQWLrL51DgF3HiA1MprV2LZ9e7KAn8bQuvGDwyeZSGgsVH9MVJvacruhQqgMV//ZDauePVrcUPeOS0RKdgEMUSOSxlVzgIg0dDFvKrydvXkEiemJMBv7BaTV6j9zqXfuh9iru7Hl8O/Y9O1eiIq0j3xA4iw1zyPiQtkQgrSULMyNrBEY5sfWaBWE6jfjaDIzArT6L4Q0n2soTotFdWU5ChOfQUJcEis+/QkyUq/mZS+AF1pXVlZUww23C0jkRNX5ZTvY9MCscQtaPaUo4N1BIBQK4FuQHflxLULuuTMzZRFxUVQUljLT6Zm//8RzQm1vKsvLkZOUAqPJ/E2GKVBDxkIb6TFxLb5PCSkpfL5zMyvMfC5cQ8oxD3a9krYmW2Om0X/6uUF3HyI5Ioqd1G16u7DOYENo3YLStZpDSFSEFW4NoU4sTUHKdzXimbYjfxX5LkaI+P44CoITIaYih/it15k+ICwhCuU+1mztt7qyit2+oqz55OHXxa5vL9w5dAyV85t2nqkoo6ASITFRZFx9CoWuRs8LR39WWJILc3lGATRGO8Js9UQk7r2H8O+O1d+BiDD6TJsEK9fudSLh3q++ZR1pJVdzGI4ehsqCUkTeCkLw7M+ZVwtMjF/pcZA4OKLv+CbXP41Ig1SD1LeoJ25QhSaPSFgLXafK1cYj33s8QiHdR1sQHu3NjIIth8+vEwlrIb8VZStn3PG6AVOTQcx7sa0JifDA3UfHUFHBgbCoOKppnZhbI0oBxXggdAn6XFOWzJePbKQKx0NJXR2jpnwK/8cPkZudCQVjY/SYuwh2Di7vpBl9R0VZTROLVm1ETmY6UpPiIC4uASNzm3abJBYgQICAjgitwF7ZsgPlnDKIy0ujoqCUTfUNmf8J+n8y7Y16Y1H6royFFkSk+VuCyHc2ROzdEOYH2NLf1bSeKq+mgtt7DyFi1Ul2naiEOByGD2Ye4ySIJoaGI+ShB6tNSWCy69+b5/65VVV1dSc/yBqG3e75xGDDLZqKnCLYbPsUktr103b68wZASl8VcX9fZdOEmdeeIu20F/uarI0u23pRdDJhDfbK8pr10PbAurcLLv61jTXxabKxMZnXA1gdnXnDH2oD7Zn9TZ5XFNJOetWkBFdUsTrWcsN0xG+7gfh/byJ+642a50RUBIqa6vh49cq6+/O5eA3HfloHaUM16HzSh73O+T7ROLX2D0Q+9sX03358pTqHVtEdhg0C6MJHyKsl8vot9hrRJGFj6H2uxzVFQNQj3HnkA3kd/lOWjWEiYaUSXDR1QUN5ASQWJmYzsbAhFZVluPv4JlRsXHhEQkJETALaruMQfvpPnL19E2ZGXdHW5BVk4OLt7cjKTmT1KLe6il1qeSJ0H0pcVZZ+TMGEKUJxKEYh+rtMRX5BBlIz49iggL3DWNhauKKwTK7NanUBgJFhf3yq2xNpGTGoqqqEqoouZKUVEZNaBqQKnud3ibJy/gFL/BAIhQKaiF97l32L+LAwGP1vJJR7W7ECg6baEnfdwfYFy/DVkT1Q038za2/CoqLsUpHffGJkZX4JxA1bt5pJo/4kCo5csgCZCUms4FI3MmATk5Hevjj47c8ozsmFpKYS6zze2PEfzLo7YtaGNXVdbKNOdog5cARVxWV8E5RzHoaxDih1Q2u9VQqC4lGWlgfjFaN4VnIJ+rvWFFcUrDwKRUcTmKwYzbwYKTCk9vvJoJlup2lsiPaix4QxeHj8NKJ+OgOjr0dCQlORXc9JzUXcH5dZkp6+rTWitt2oKzrl1FQwbsUSRPk+ReS5J1Dpaw3FbqZQcDJha8jlOYXIuh6A4sAkDF1Y7xnidvw0wj29mUk1rcfUojG+G2I3XsKRH37BqN1bIA7+4TOvQuMu6pEqDiTRfMCPFKRRVJzP/r9h0VGq8/qryCmhaZCQU+YJBWmIgoE1MgMfIFO6FIqqNa9DWxET5oMbD/ZBycwBVWWlKEwKh5bjEChbOEFETBL5CaFIfXwFccUR9OkCUtKy6Dt4AoZOmAkZOXm2Ziyg/VFW02AXAQIECPjQoAm+c+v/htqILtD+yIUFf5CwlXrmMQuFExUXY356bwoSn6jmbA6qVVnd2koxqdvo4XAaNYxt8pAvN4VXUJ1anJePfz9bgihvP7aCSz6GnH25kFVRwox1P8HMqUawMbCvsXLJ9QiH6kC7Jvef6/6MNXKFGk3lZV7zZyvTDUXCWuh+kg89ZOu8nY9+yZ53YUmxui2airwStu6rNbTtty1qUTfQh/2gvgjdeRuicpJsGpA2dig4j4TL3IdhsO7jiti7gWwCk6CtlM6D+sPCpRuO/rAWuV6RUHI2g9mqCSjPLEBJbAZKE7KQtPceRiyezzzca9fKT/yyAaqD7WH45bC6gBnyM8/xCIf/r+fYdGB3PmvKr0KtQCieXv9eqU4rZT7ZYs148tXWqtyEEog3Iwo3hiYJmUj4HLLeIbGwsVSQk5GD8vJSyOvz3+SRVtWBuIwCUspTodsG9W9DOCVFOHniT1QJC0PTcQjSntyEvL4VNLsOhKSKFjg5aUh/egc5ccHIQUZNorNZFwzqNQaaeuZ877PlUoiAliMDVYN6D3/Bc/xuUs5peXNNIBQK4IGKkegnT2G+9iMe4UbWSoddF/LZbtw7eAyTf/j6jRwPCXe2fV0ReTOIJbUJNzoxUmJbSVwG7Jb1fqX7l5CWhq5l/UkmJSIKu79cARlrHdj+PglSeipsKpAKjbh/rmPPkm+weO9WdlzO40ex1Lu4f67BaPmoOk9BGp0nP0EKJaGCJWjWdij1toSwtARy7oaw2zQMOGlIrWcidXkpuKWhn0pZej7STnjBtm8vFn7SXiioq+KzrX9gz9KVCJyzA7LmNV3LoohUyCgpYMG/f8LAzoYVVRmxCRCTlGBTpvRYLV2dsXnWZwhbegiak7pD1loXlQUlyLrqjzyfaHy0eiV7zmufJ/eTZ5kY3fC9RtDrTD6PAY+2IfaeG+y6vdwzsrVExIWxMfrsvExUowocbin0YQolIV6vywLhXGir6tT93d6l5v3yOD75tVNn40OVEexXznxwKCW7MVXPO/W29gZQUecvJr4K9Nyf2/Mt5PUsoWrdA5EXtsJgwHQom9V3iZXNHCCrZYLwUxvRe/AoTJy9WDAxKECAAAEC3gjkWXx1+24o97OB4eKhddeLKcuyibfqsgrc3H0APSaOfWNpsPb9+7CpMwrEaxzWRhsf2TeCYNev1yvZ9JD4oWFkwNO4p8C8lLg4mP4wHorOZqxRTCIXbaZQmN/SQ7ugZWrMmveWPZ0Rs+8BZC21eVJ+SRhLPeIBKTk5RP54kjVxpYzUUBqXiYrcYr7heex4RITZ12iiruhZCuTt6+sdqosTd99mE1wkcLYnU376FvuW/4CINWdY815cTQ4lsZmoLOZgyII5GDx/NtuyIUse2lLRNjdhQYH0/AXcuY/QX8+xhGSVPta0L4nCkCRknPeBmbMjExRr8Tp3mQ1G0HurcQq1sqsFsp1M4HbiTJsJhYSLkiGepCfCVlIa96+eQdTdG6isLocnbrKpQi0Y8ti/5COHvU/6Wtq/lh80v9o1PeW5HVA5f+shmu6j0BAdXdXXrn0bc+PsYZQW58Py45WIurQd8vqWMB42h3mL127YGA35BLE394Obn4pf/j0GCcnmG/wCBAhontKS+q26lyEQCgXw4Hf9NqT0VNlqbGNoak5lsD38LtzCpO+Xv7F1D/IMDJ69EDG/X4D+ZwNZR5mEDgrSoAk3bQtTWLm2jT/Knf1HIKosC9OfJtb5oVCxREUCdXJpNZjEVHNnRxaeMuO31Ti48icEf7Idin2s2JpwvlcUK8wGzZ0Fp9HD4HHiLILdPFBWUQEtbX3EZwWDk5zDVyzkJGWzP6vzOQhdsBcqQztBUkcJxZFpyL4ZBDkFBYxfsRTtDQmBqy6fYu8H8mshTD6eiS5DBtSsc5DJsIY6uzSEitUl+3fgwl9bEbb1et3EoYaJIWZt/AWdBvatu21ZSSlyklJhPK3eV6YhNEkpa6aFXFor5w3qe21ueVzGwfM7ICssD71qU3ZdOpLgiwcw49rDQKhGDMzipiKXm4lpzu2TnGbn6IqrJ/ejICEUCoZNw11ywr2hqWfU5hNlqYmxSEuKg/HweciJeAJxeRUomTZNKxaXVYSSmSN8Ht7G5E+XtOkxCPhwod/fb3JdUIAAAe8esf5BKMzIhtV3/BuF5JEcdOUpIjy9Yduv1xs5JvLi07IwRcxvF2C4fCTk7PXZ7zKaVEvYeZttjPT/vW08ZMM9fRAfEAKL36dCvlO9gEgrwWY/TUTIZ3tw78BRTP3lB3Y9rdBum/cFghfuZSF6knoqzP86zzMSmmbGmLd5A/Mdf3zxCvLvRkJeVRklMlKsHm3u93R5Sh7kVFRY7Ute4WQ3QxY0VI9Sk37a2h8ho9h2Gx/8oObyZ9v+ZO+Hp9dvo7SwCCrd+6Hb2OFs8pIgodjEgbeGIbF21vo1uLlrP0uqTj/rXXN/sjLo/dFEDFs0lzW4a0kJj2Rr1fw2hAjakCGBtq3PXwXpKVj71y8oKSyERrUuNKCDPGTjGZ4iFQnowu0FUSFRFoKSIBwJ2y492iU0jgLS1LT1kBPuw7ZZGpMXG4zKshLYObZ9kIr3w9tQMLJjwSnlBdkw6DelTiSshZ5zzS4DEX52E2LCQ2DVif9nBwECWougJm0egVAogIfSwkKIa8g3+w9GXEMB5SWlzOC3dly/vaFptdkb1+Lw92sQOHs7ZIzVUVnIYUllujYWmLvp9zaZdKLuY+Dt+9Ca3pOvaTKZJUvpqsL/1l0mFBL2A/pg+fH/4HbsDEIfeaK6shJGNtboteLbutuMWf4FuxDU7fx5+ASknvKC8YrRPM9z7SSiopY6Ptu2CfcPHYPf6Vuo4JRBSl4OPcePYabWZOT9JiBB0HncSHZpDWoGepi7eT0zHc9JSWFdbA3jpinKIs+nQ6uKmve3obVuCk55EVVVVXgc6IZ73jeQmZsOGSk59OzcF326DeabVByfEoND53dCD6Ywr+5Ud1zGXGtEIxiRCIQoVwzFKECSUAw6WzrBya4H2gNDMyuY2nRB/MNTEJGQhqxWzWQldW1pzSI/Lhjjlqxq8xNYSVEh+5PWnitKCiGppNGkKKtFUlkTmcFughNpA8rLOEiKi2IeojqGJpCQ5PXyFMB/Osj91iXcv3YWaYlxzPu1i3NvDBozBbpGTU3dBQgQ8GFTUvD8PPXc/oRfPVpzu4I3dkxUa362ZSP2LFuJ8JVH2YSbqKwEimMy2HbF7I2/QM/Gsk1+VsCte5DWV2NiZJPjkBCDyhB7+J+4hylrvmfnZnlVFSw7tBte5y7B+/I15AcFQUFdDX2Xf8FSfcmf22XiGHap5cKfW/HowiVoTXRmzdmGkMBYmpSNz/79k/l1U7BgzJ1gtv1Agmn/H36EcZf2CdVrDD0++lmt/XlkKzR80TwM/HQmUiOjmAWzlpkxey6a3FZcnNWczUE2RCQsvqwOSgoLh9vxs4j2D6Btb5g7OaLnx+PZ5GdjqK56sHMTqgur0KN6CCSE6m2U8rnZ8MNDhMEXSlw1JhJCCpg4p+bzRFtDj2vo+Ok4tHUd0nxvQb1TXwiLirFjLEqJRrL7GVjaO0LPmP+q7+vWpOJ6+qgoqfm3LKms1Ww9SuTn1AxVCKh5D1Hzv7iwACrqWgKrmhYSFRaI2xeOI9iPQosqoWdsgX7DJ6B73yGCzzoNEAiFAnig7ly4ny+qK6rqVmkbQl6FChpqb0wkrIUSwn66eR6PTp3D0xt3kFmYz9ZTyb/F++JVuE4ez/xcXgdKdyYhT0yF//3QLw4xVVkW9tIQSkie9MPyF943fU98UAiqq6oxaM4MnNuwma02UOqxpL4KS2hLPe7B/GWm/7YaGkb6+OjHb9jkJjPwlpJ8K4nTr7vCTJfmIKGAVj9SbwVBbXiXJqseRc+SUZKQCZ3pzZsml1eU46/9axAS4Q95ewNIdzVBUUoujl/bj1teV/Dd/N+gqsQ79Xjn0RVICEvBrNqe52RA/2/CtUUaElhhRsm/I10mYNygKWy9pj2gn7ngm7XYsmY5W/+VVtWGqLQ8SjMTUVFajNFT58G5X/3KVVuhoqFNPxxFabEQk5ZDYRIlGVfzFQs5OamQU1AWnDjpd0RFBS4d24MH188zTx1CQkoGvYeMZq8V+XcK4P+8bV+3EqFPH7PJWZ2e41BZWoQAvyd44n4XC1b+1i5TCgIECHh3qZ0WK36WzJPU27AeJZSf3+5NQdYvJMhFPH6C+wePITk8itUvtH0SeOc+lLQ0oWNRs6nwOnCKiiCqItt8415VDpVl5WzluXYyTlJWBn1nfMwuL2qK05puUW4eLFy6w/faTYR/c5SFd9BaclVpTepx8oGHbJ3Z3NmJefP1mzkF5RwOREXFeCbx3gVo4pA2ZV6Eda8e8F91l61209RmQ2jVOvt2MAtXeRGPTl/A6d/+hISaPBRdzJmtjN/9u/A6fwlT13zPAmoaEhMUiNyUBHRBLx6RkFAQUoE+1xyxeIZ0JMK+qysmfLIYGjptu/bbEJcBI5CTmYYrJ/YhK9gNUmq6qCzOR0l2KgzNbTDv61/a5eeqaekgNT2O+RLW1p21jfOGlOaksj8VlOtX6z9kArzdcP7wbqQmRNdcISQE687dMPGTxdDWf7UgyA+BR3eu4NDW3yGppA71LgMhLCaB3PgQ7N/8C8KD/TBz8beCzzzPebd+0wtod8h74/6h40i/4MM6jA0pjc9Ezr0QDJoz860cW3FuHh4eO42SoiIo97eGpK4ySqIzcH3XPjy5ehOLd29hviSvCplik0E0dVFpbaM2QKQWCiUpiUiDmlOfVn1AvvLPTnievYDykhrfDxI4DeyskeGXiOC7u5nBNE0mySgrYuraH9B12ECe7rWkzPvrwzFg1jTsWLiMpdHpzelXt/JRHJWG2N8vspVlra5dgCz+33/6+iGExQbD4rcpbOKzFgpeiVx5HNuObsTqRRt5viciNgwq1ZoQ5iOK0YlBnauLQrkc/PHtbog3SiJuD2TlFfHN+p2sq+X36C5KS0qg4eAA10GjoKHNmzzHD5ruTYiJQBmnBOpaelBS5RVG+aGkogbbrs6I8r8HbdexyA7zQm7kUyib15sUE+VFuciNfIIBIyfiQ4ee5x3rv0PoU2+o2vWCgUkX9m83L9ofdy6fRlJcNBav2ggREcFptTF3Lp1EqL8PjIfPh7xefXq8RpcBiLt5AHv/+gm/7z0HST4TwAIECPgwIbFNx9oCqccesTRhmqKrhZrZKYfdoKqvC+Ound74sZEARE1qWg9W6GoMHceuqCriIPjOY9bMnrXhF9bgfh0UNTVQ4ukFTkou37CRwqBEKGprtEq0C7r3EJf+3o6shKS669SN9JnAELXmTF09KiQiAqeRQzB+5Vd1H5jpT36TeO8LnQb1w7UdexG99hxMvh/L0p4JWrVO2HELnJQc9Pv14xdOEpJIqD6yK7NJqg0srJ43APH/XMOxH3+DnrUl1A3rhb640BCICIlBmcu/blOHDmIRhs9/2AB7R1e0N/Qaj5oyF069B8Hj1iVkpCZDStoIjj3/B+su3Vs0sJCVnoKs9FRIy8pBz8isRYILNVv3/LEaquUcZoWT7ncbMsPn8jSvaXIu/eltKKqow8K27VOX3zW8H9zEf5t+ZjUV2QjRhlBxejxiA+5iw8qFWLF+B7T1mtqIfeiQEH743/UsuFGvz6S695iabU9mxeR55ygs7R3Qvc+Qt32oHQLBJxoBPNCKaL/ZU3Fv71FmdKw6yJ6JN/neUUg//wRq+nroPXXSWzm2I6vWokK0Gja75kJcpSZ5mNCc7IyIr4/gzO+bWCrxq0C+Jzf3HkRRdi7gkYv8JzFQHWQHnZm9ISonxU5QKcceoYpT1mIjY+raHljxI8I8PKE50RnK/W3YlGbuowgkH3GHMJ5PqXHrb1+QmfVBrXjSevakVV/jzG9/saAXWWsdVBZwUByVCnVjA8zf8gciy8r5fi+nnIO73tehMd6JRyQkKARGd+FARP18GjGJkTDWq59GoOnASlQ3e0zVqIa4uMQbEQnrjklEBPZOruzSGtxvXcSVE/uRm5XO/k4nPFuHHvho3lKo0tTgC6D1lQ3fLECy+1lIqeoi4f5xlOVnQsWyG4TFJVEQH4q0J9chJ6eAAaOaL44/pM5t8JNHMB42l8e/R1pVF7LaZgi7shN+j+7DqVe90C+g5vcambQrmXblEQkJYRFR6PYaj5Aja1nR23vouLd2nAIECOh4TFy5DP/OX4KwZYegMcGJeTvTBkb6OR9wYjMwdcvGt7JtQWnMT6/fgcl3Y6Hcq2YKitD62BUx6y/g0Hc/48drZyCjIN/q+86IT8CVLbsQfM+NCZJBn+5gQqn2jF6Qs64JUKG04Zz7oRj62Sctvl//m3eZp7aCozEsFk2FpA4129ORevwRisNTICopgUpOzeotbRhwiotRVlz8xoJi3ja05bJg21/Yseh/CF6wB7IW2iyEsCgkEUIQwvS1q144lUjrxpLqCjwiYd1wwOKhyPeJYV6J41bU+z0LiwiD+/w/+hmNoaA9QkFRGW8STR0DTJi9uFXfkxwfgxN7/kZEkG/ddeR5OHbafDi41ofG8KNrj36wc7yJkJv7IatrzurPmGt7odFlIKQo9Ti3JvU4Py6ETTV+6MF65WVlOLbrLyiZdoHBgGl1YhfZCCka2SHi3Gac2bcVX/z459s+1A6H282LEBIRg47r2CZbVMrmjsiNeIL7V88KhMLnCIRCAU0Y+eUCKGlq4O6BIwi/c5RdJyohDsfhQzBy6UK22vCmodWOOP8gmPwwnkckJKR0VaD5sQsCd99hvngvWnflx/2Dx3Fx0za2cmG6eiITBgv8Ypgwmu8bA+3pvZH7IBR53lEYuWQBlLX5e2c0JsLLByH33WG6agKUniflEsp9rJF2ygtcYWEYzh0GeQdjVBWUIuPaU1zevANFOXkY/dUitAWF2TkIuvuQef2o6umwDjf5sHQkeowfzcJoqPBOjYyGmJYEbOcvZOnOrFMel8j3+xJTYlHGKYVST/5+QIpOJhARF0N4bAiPUNjJygE30i+iilsJESHeX4FV3CpkCiejt+UgdHQoJe7cwe2sUDBznVSzQpwchQj/u1j/zQJ8u3EXlNU0X1gI0iTj2QP/IsDbnSnWaX63kOZ7s+42lp2cMHPxSsgrtp8vZmlxEbzuX0dcZBhERUVh09UZnbr1euP2Bi/D7eYlyGoa8jX5JgFMTtuEefAJhMKm3kMkZBt24b9CLy6rBBk1XcRHh7/xYxMgQEDHhoSZxXu34fI/2xH5x+W664262mPkru9h1PnNeOQ15uHx01ByNuMRCQlqBhssGoKAmdvgc/HqC1eA+ZEeE4d/PvkcXBlR6H02ADJmmuAk5yLtnDfCVxyBzuw+qMwvQeYVf+iYm6JXCxv3ZKtzduNmVouafDeuzuqF6ukct2cs1VhleKea4QApceT7ROHZsUfYMmcRS1aWluetu18FOoYQt0fIjEuAhIw0q0cbB+K9bchj+9uzhxFw5wHC3DxRWV4O3fkD0X3sSMipvFisi/J7CgWXmmTqxpDvuYKzKaL8/Hmut3BwQjW3EhlIhiaabpBQmImsrAJ0DF5/lb09SUmIwcZvF0BIQhYG/adCRsMQ5YU5yAx6iN0bV7FandaaX+j9+c2vuHpyH+5fP8+uK0gMR0FCWN1tlNQ0MX/FWnR16deujc2wAB/4ez1ga/baBsbo0X9Eu9bAr0LA44coLS6EkdPQJmKXiIQU85cMeXgKudmZbINIQD0J0eGQ1TaBiBj/BoicvjUSvK+88ePqqHSsT2ICOgQ0zdbzo/HM8DgtOpatz9Ik4et6AL4O5KdCKHbnf7JU7GHGVgOSwyNbJRTmpWfg0j/boTmhO3Q/7Vc3ySdnowvl3tYI+XIfYjdeZIlxM9atRpehLRcBSPiSNtJgx9aQ1JOezO/E5p9PmMcMQ00ehouHQkJTCff3HkePCaNZwfI6a5KX/v4XbsfPsAlFURlJVBSUQFpJAZO+W86TPtwRoGJxSCs640Td1CW5UzcDv+nMAT2G44bbRQRXecOG6wRRoZp1pkpuBULhi0pUYpBr6wJc3jT5udm4cHgX1Dv3g47zqLrrJRTUoGBog4gzm3D5+H+Y+cV3L7wf8rpZ+N3vKMjLZWIOFWvZGWmorChnhtUtWX1+HYKePGLrJuXlZZBR10N1ZTk8bl+GqqYuvlz9J0vh6yhkZaRASq0+ebIxUmr6yExtG7GL3rcx4cEs9INWvaw7d2drPO8ios/X4qorypp9rNXlZcz3SoAAAQIao2dtgYU7/kZuWjrbuiDBpqUN2/aAxKP0qFgYjuIvfFAoiKyVDhJDnrX6vknMg4IErP6czprWhKyVLmswR6w6gaR995nI1mviOAz+7JMWrwI/83yMoqwc2KypFwmJovAUZN8OguGSYVAbWp8arD7SAfJdjRG66D88PHoKQxfMwesQ5vEYx376jR2DmJw0KkvLcG7jZnQfMwLjVy5joSMdBWqmOwwbxC6tgdWa1c3Xo/S1xvWotrEJtCztERkRAKlqGSgIKdcHVCAByYjBqDHzOtTzww9qOAtJyMBs7JdMqCIkFFQhq2OGhHvHcXLPZjZV+KLgN3qMo6fNx7DJs5GeFM+eAwroKCzIg7yiMsysO7XrJGFBXg62/vI1EqKfQUpJA6KSMvDxuIuLR3dj6oKv4Tqw43wuyM5IhZikDKv5+SGtrs8+G+VmpreJUEiCY3igL6qrq2Bobv1OrzRTrdlcPUpUV3AgIqhH34xQuG7dOpw9exbPnj2DFKVtubhg/fr1sLDgXT8S0DGhX8ja5h2jiyXy/CRZXVbBxvgbU82pYH+29mTqfeEqhMXFoD3FtckJXMpAFWpDOqHQLRL/O/YfRFp5gsrLyICUsRpvsjEzRA6C2oiu9SJhAzRGOyD9pBd8Ll3D8MXz8apQkp37ibNsVUV9RBdWcJYmZiP5wAMc/OZHzN/6BzOnJrKTUuB+nFKbvZgobGBjzYTiN5Vm96roaRlCSloGOQ/DIGPW9ENDrlckqisqYWVsx3M9hZssmfUdNh/4DW5VV6DCpYQwIeQIpQPCwBfTV0JLTQcdmccPbtA/UObx1hgxaXmoWLvA++EtfDz/K4hL8Bpk84O6pbUdU/KUeRNQavCO37+FrK4lzHpNhJhMzYpWSVYS4m8fxt+rl+KnLUcgLtEx1p5kZBWQU5jb7NfLC7MhL1+Twvk60GTlgS2/ITUhpu46CknpP2oSxkyd/86t3JDvoLGlHTLDvZknTOPfs8XpcSjNy2Ar8wIEtBeCevTdhzZd6PK2odRf8vSr5vC3RamtSWvr1paSnZyCSK8nMFo+sk4kbDipqPtJX4QtPYBpa75nGxetIT89ExAWgpQhr2hAoSXi6vJskrAx5ItIljle5y+/llAYFxCM/5athGwnfSZUShups3ThzJsB8N53FZUVlZi6pqapSeF9Ty5fh/elqyjIyoGCmgq6jR4Oh+FDOvwKtLmjA566PYDevAFNpgrps0u+ZxRcxtQ3dmvpNfcLeG7bCJ/Yu1ASUoNEtRQKRfJQXFWA7n2HsiTijkxeTibz2NbrPalOJKyFzveajoMRGvkETz0ftCicT0xMHLpvqA6thUTJbb9+g7S0FJiO/hyyWibs2Cs5xUjxuoxD235n/t8UFNIRkJaTR2U5h4XCiUo1HeIpK8hhf8rItd76oCGc0hIc3fEHfNxuMRuEWsxtu2L2ku9fuLXUUaFaM8BnI8oKsiEhzxuKw62uQl6kL+wdBfVoLe1q7PHgwQMsWrQIXl5euHXrFioqKjB48GAUF/OmxgrAa02Ohbo9wtnfN+Hk2o3wOncZ5aU1oRnvE2bdHdiJl0Q2fmTdDmJdVoNOL041a/J9CUmsaKkN0WiMnK0eSvMKUF5S2upjllNWQlkyr7BAaXJUIMmY8C92aT2BUpDz0jLwqtD6tfvJs9CZ1RvaH7vUFZxSeiow+XYs605f2763rsu7fuIMPLp0GbBSgXg3PYSH+GPrnEW4sXMfOjIS4pIY0H0YMs4/Qd7jSJ6vUWpd0vbbsDCxhYFO0+SvTpaO+HPlboweOBnKRkpQMlTAiP7j2XUOtrwhPh2RnIw0SCqqQlRCutluIk0FFubnoaNy+8JxlvBsOGhmnUhY6/lnNOQT5GSk4vH96+goOPcdwnxzOHlN/22StyN553Tvw5to+CrrO3+t+hIFnEqYjFyAzvP/gO2M1VC27Y2bZ48w/593EfqgU5gag2TPi6iuqP9wXZKVjIS7R6Gtb8JWzgUIaC8E9eibgfz1rm3bgxNr1uP6jv9YI/J9g2wxzLs7IudOCBMYGlMSl8k8BK1cu7fqfrMSk9mfcjb8J/llzLVqfK5foT5kQX/VXJSl8Nak5ZmFkDJU57suS0ibaKAgo8Y7+1W5vvM/SOqpwPTHiazeJqjm1hzXDXqfDcSTS9fY+6Y4Lx//zF6A0+v+RIFUBSR7GiFfogyn1v6BrZ8uQmlhIToyPT8ej/LcIsRtuc7CdhqKhLGbrjJh2XVyUx9eSVl5fLNhF+Yu/xm6Xc0gbS4Pm549sOyXLZi95IcO3xzMzcpk02vSavzftyTGiEvJshCJjgpNy8VHhkK/31TIaZvWNTRpqpACL6TVdHHxyG50FLr26Mu8WTODyTaIF/IXzQp6yLaC1F9jK4j0hX9//Qa+ng+g4zIGdp/8ik7zNsBw4EzEJ8Rj43eLUFSQj3cNCuqRV1RB3M39KMuvT8qsLCtFwv0T4BRkY+CYKW/1GD+YicLr13k/5O3fvx/q6urw9fVF79692/NHfxDkpKRi1xcrkBETBykdFQhLisHr7CW2cjpr/RoWFPG+oKiuhq7DBsF//z1IaClBwamm28Ot5iLnfgjSz3qj38wprU5kI7/FiuxCdj8N1zFqKc8sYCdpsVfoZDqOGIrg5T+gIDAB8vY1KWf0GgmJi4KTXNPtaQxNHJal5kG266v7YQTeuc863mojujT5GhWD6mMcEf3bObbOvf/rH1iX1/jbsRCRrOl+c+f2R+oxD9zY8R/0baxg1bPjfoCfMHgakjIS4P/TachZ6EDKQhPlqfnIfxINDXUdLJ66otnvVVJQwfjBUwG6vGNQUnJ5YR6qK2nCVoyvcEW+JR15XdX/sRuUrFxYoEVjyJBZRtMQZw9uR7c+g1+4rvKm6N53CG5fOoGYyztYUrSCATUlhFCQEIrkR+ehoq75Qg+elkDr4sKSMjAZtbDOP0VMRgHa3YZBVFIaD66fw8DRH0GtA61ktwT7bj0x6dMlOP3fFuSG+0Ba0xBVnGKW0KehY4DFqza8lUACAR8Ognq0faEPlWfX/41Hp86z1VIJLUVwbuXi5q796DN1EkZ9tei9+jfeb9YU7Fz4FRJ23obu7L519RPVdjHrzkNJRxN2A/q06j5r/b+p7pTQVGzy9cq8EiZASb2CT7ilqzOk5OWQetoLRkuG110vpiiNotCkZkP06PHIKCm8csBecX4BIjx92GoziZyNoUnGpP/uwe/qLaTFxCIzLRU2Wz5hoTV19xGVhojvjuP0ur8w47fV6KjQBtbHq7/F8Z/XoeBxFBSczdjzmu8ZierSckz/bTXzCucHbUM59hzILu8ass83KcoLsiCt2vTx0dRbBaek7nYdNaxOUkGFrUo3hmppFUtnxD08hVB/7w4xVUifAQaN+RjXzx5m/uKqtr0gJiULTm46Un2uoSg1BrN+2PBawZjBvp6ICPZjTWt53XqffSXTzpDRMMCzk+vx4NpZjPiodbZRbxtJKWks+WkT/vn5K4QeXwdZTSMIi4qjOC2GRgoxZ9lqGJrxes9+yLxRj8L8/BrlWVmZvyFsWVkZu9RSUFDwxo7tXaOirAzbFy5DcUUJrP6aCRlLbfYLoSw9D/FbbmDP0m/wv6N7WYrx+8LE7/6HwqwcRKw+BWlDdUjoKoMTk4HSlBx0GToAwz6f2+r77Dy4P1vRpaASMqZuSHV5JbKuB8CqVw94nrnIpg+pkOs0qB+0zUxeet82fVxh2NkO0WvOMANq5T5WLGGOCuj08z5QH+UAUVnetdDsu8EozymEw4hXn0yi4BLq1pIvIT8kNGtO1p5nL6GqqhJG/xtRV+QS9D7SmuKK/MfReHD0ZIcWCslrYtnM7+EX6o373jeR4Z0GdWl5jBv3OVwc+kFS/OVrt+8ijr0G4PLxvch+5g01W96kZPL5yw55BDsnF7aa3VGpKC+DSDMTkYSolBzy0xNw89wRjJrS+n/b7VFc/G/tFuz54ydE3djPCguqwaoqymFkYYd5y39+reebVjz8vR5Cy3kUX5NlVaseSPe9Ce+HNzHio9fzi3obDBg1maV6U1J3SkIsJCS00HnGHHTu3qfD+y8JeP8Q1KNtC00Pep65AP0FA6E2rAvbjqBJqvRLfnjw3ylIKypg0NyZeF+wcHbChG+/wtn1m5B7NwQydnqoKipDYVACFDXV8dm/f7b695qetSWUtDWRftEXsrZ6TT7kp196woIFaaWZAviowW3YyRa2fXrWBL+9AFrbHb5oHs6s+4tNFmpNcgZXSKimcZ2Ug1yPcCg3CoaryCtGzq1guI4djVeFU1jE/pTQaCp8EvQ+EVWQQdSTp4j1D4T+oiE8IiEhY6oJ7Wk9EbD7LkYvXdTqwMI3idOoodC3sWTpxlFP/VmSscuokXCZPA5q+u9Wg6+lqGnqwNDMmgWXKBjaQkiYVxDODHaDsLAQuvToWN7oDSl/Xo82J6yJStXUdrSC/OvO0x2i6UF+jjQUcvPcUaT53YaouCSbipORV8Tcr9e8tp0LhQxS0FxDkbAWcTklKJp0waO71945oZDQMTDGL9uPw8ftNhNEKysrYNizJ3oOGgUF5Y77++W9FgopSWjp0qVwdXWFra1tsx4yP//885s6pHeagFv3kJ2QDNsd85iXXi10MjZdNR7Bc3fhwdFTmPzD13hfoGnB+dv+QOTjJ/C5fB2FOblQcjJGtzEjYNTZ7pU6J0Zd7GHm7IjYPy6jetFglqBLXc/S+Cwk7r6DsrR8PEv3Qqi7J5varMgtwq3dB2A/qC+mrvmhiWcKrY6Qv2B+RiZklZQwetkiPDx6Ev7/3kD8jltAVb3HA6Xi6c7uw0yjq4pKkXk9ACmH3djkpI7Fq/tzyCgqsAKPRGN+xRml2xExTwMh18kAovJNxRp6LhV7WSD2yKMW/1zqnJYWFrE/KSHvdTpZrUFYWASOtj3Y5X0jKz0VWekpbDKQvANrn1NKLHYZOBKed8+jqrwUqtY9WJFTkhGP1MdXUFGci5EdXEzSMTBBXtIzqNs3neYhsbMoJQpSypp4eP0CRkz+pEOs3ygqq2H5b9uQEBPBOq2EmXVnGJjyT95ubTowGUVLKvFPgRQWE4eEnDILnnmXP1CMm7HwbR+GgA8cQT3atnCKilmdoznJGRpjnOquF5YQg9bE7mxr496hY+gz/aMO7zPXGmiNlLyePc9eRHJ4FMSUxWH74xR0GTIA4lKtb1KS+DBs4ac4uupXJGy/Ca2PXSGuLIvKYg4yLvsh9bgnq60OrfwJkhqKbEPkweETUNTSwKeb1jWpG8tKS/H0+h3EB4WwOsmihxMLDrmydReybgXxBMFF/34B2d2CYbhsBESlJZDvE41kCk4Rl0Dv6ZNf+TmSVVFix1n0LBnyXZoOLlCtWp6Rj/SyBCZ8Kvfify6l6ymwkB6LfQsnNcnvsKykBNJyci8VUtsSGtCg5/l9g1NajMSYGpsfPWMz5j9cy9jpn2Hzz8sQe2MfNJ2GscnCipICZAa5Id3/LoZNnAk5hY6VHNwQXQMTPLpzhR0z+Xw3piDhGUQlZVk4yLMAH1h3aZ2tQHtAvy/GTJuPAaM+YinIxUUFUNXQhp2TK/N5fF2o1hRXaD6VXEJRHVlxgXhXIf92CqjpSCE1HZE39puTvGGCg4Ph7t50n76Wb7/9Fl999RVPB1dPr31TN99VAu8+ZP55DUXChsWZ8gBbBN64/14JhbW/GKkwqw3ieF1IePlk41oc/uEXhG64CNFtNyEiJY6yrAKISUmiurISqkM6QXdmb4gpy6K6sgo590MRsu0mjq3+DbPW13yQIHHs8ubtuHfgGERlpSClqwJOWi7u7j8CfTtrmgyHsqsFNMY6QUJDgYl1KUfc2NpKwvZb7D6okOkxfgzG/G/xaz0mXSsLZlqdfPAhjJaP4hHsKgtK2Jo2vUeqKisB7gvEF0pve4nYR0WY+8lzrFglfxlanSZUDfXQf+ZUdB874o0Jhu8TyfExOLl3M8IDn9Rdp6athzFT58GxZ02AybQFX0NCQhIPrp9Hqvc1CIuKslVkZXUtzF/9F/RNOrZJf9/h43Fwy2/Iiw2GolH9h3X6t5TifQ1VZRyoODkjyf0sSooL2apFR0Hf2JxdiPKyMpbUTOEx5NeiqqHFupLkudearjOt5VDSWmlWMuT1mn5YqirngJOf1SYJdgIEfMgI6tG2JcLbF+UlHDZJyA+14V3YFkX0k6cdekPhVaBV0lFL2q754ThyKEqLilk9mXnVHxIq8sz3jltVxdJ4q2REYLV6BmQsdVhtRVsqcZuvYfuCpfj6xIG6aTua0Nu3/AeUFhRC1kSTbcjQxCeJihCqCSqhKT1qFlfmFjMhMvO6P/w9/66p+7hc6NlaYdrmH5j1z+s0+CVkZJB+8QlUB9tDXEWO91x/1IP9f1VFTSAhiYX8qPNIfEk9GebuhRu79zNrndp6VFxaCs7jRmLgnBk1Xo0CWr39ce7QDrjfuoRyTo1fu7iEFHoOGomxMxaywDnLTo5YuHIdDm/fgPDTf7KNC2r4iopLYMTk2R1+C4JCY84c+BdJHudhOGAaz1RkUVosciKesPDAzIB7SEmM7RBCYcPa0XVQfUhOVFgg3G6cZ58jyLanS48+6NF/OGRkWxdsoqSqhuTgoGZtCUqzk6Go0ryQKOD94I0IhYsXL8bly5fx8OFD6Oo2P3otISHBLgJeDnUKRRVesLanKP1ehpq0B7ROPPfv3xHm4YWTv2yoSYcTEkLF8+evsrCUrWcQlLisOtCOFSABf19FxsJPoW6oj3sHjzGRkFLp1Ec7slVe8pKh8JW4rddZ0pzxyjF1v2yVXMwh39UQYUsPQk5EBgM/mQZLl+5tUsQoaqgxkS/7bggqcouhMcYR4uoKKApLRtopL1SVlLH0Kk0jA4R5PmYdXTFF3pVJOjHkPnwGU4fOzf4cMpbeOu9LpEZGs5+n6GzG1qtZgvDDUJxcsx5JYeFsPUcgFrYcKkI2frsQQpKyMOg/FTIahigvykVm4EPs+eNHHNm+gSXC9R48BhPnfIFhk2YjyMcdHE4ptHQNYNnJqUOsRbwM575Dce30QdaBVjS2Zysr1ZVlyAl/wpJwdVzHMm+Y2tTfjkhBXg42/bgEqYmxkNcxg5i8KqJiYhCw9mt0du6DecvXMOP7lnY3SQT2f+IBFStnZqLdkIzAB6iuqmAFrQABAl4NQT3a9pSXltb53fGj9vpyjqAmbQm9Pp7AphIPfrMa0b5Pa8QzEWFUlpVBuEoS3Kr6D+4UNmL2y2QEfbKD+UMOWzQXmfGJ2P3lCubZbLZkWp3fIXn9xay/wD4b2G2dCQn1mutpapE8BMVUZJk/9YjFn8GsuyP0rNum2ahhqI/E8AiELj3AJkzl7A1QkVPExMk8r0j2GOS4UqzxnPMwDBqjHJrcB11P9bdRJ/4TwAQF8JG3NjXK6TGrj+zKtmpomvHR+UsIfuCBL/f9C3lV3qRTAc1D9kT//rYS4cH+ULPvw/zpiLxof9y7dg4Pb1xgzWmnngPQZ9g4rNt9FiF+XshMT4GMrBzsnXp2aK/sWugYR348B+cP7cCz3DRWg4lJyaEgKRy5kX4sIJDsX9J8b0KiUbJzR4E+t509sA23zh+DpKIaZLRMUMgpwpn9/7L15KVrNkNbz6jF90ee2z4PbyE/NhCKxp14vlaak4b8mECMnfFZOzwSAR+MUEhv2i+++ALnzp3D/fv3YWTU8jeogBejbWKM+CtXWJeQPD4aU/g0Dhomgue7pRTl5LLU6HKRKpiunghFJxMm9OU8CEXinruIXH0Slhum1yXDqfSzQdKuOyw4pO+Mj3H3wFEWHqI1uX79lVaY1YZ1ZkJcyjEPVBWW8qz5ikiKQ3NCN8T+dQWmTl3brNOpqKEOo672SE1PQkVeCSJ/Ov38gISg2N2UhcFkXHiCYV98hihff8RuuATj78fWeRqSCJp86CGKo9PQZ/k3zf6cS5u2Iz0+gYmEBouGsKKsFpW+1si85o9H/5yHXb9ebTYB+iFw9sC/1AKH2dgvIfLcY1FCQRWy2qZIuH8cuVFPkZqdj71//YTHD25gwcp1PN3EdwVaJZ664GtsXr0ExRnxyIsJYNeTmbTx8HmQ17VA5Lm/Wee2I4SZ8Du/7f3zJ2RnZ8Fy4v8gpaJdd31+bBACbh/C5RP/sdWQljJqyqcI9vVC1IUtUO8yCHK6ZmwVJivkEbLDvDB80iwoqQo6uAIEtBZBPdp+aD6vNQv846DYvaltSsHTOJ7bCXg517btRrSfP7Sn92ITmaLyUqzZm7TvPiJ+OA7LP2Yw7z5CTEEaSr0t8fT2XSYUuh07DSEJEZiunsDqzFro9mZrJiPo053IfxIL9eG8E6CUQpx+xpv9W2krkZDoPmYE4n8JgaytLhJ3362b9KMkZPK0TNp7DwMXL0Bi2DMEHXwIGTNNyFrWh2IUhiQh9bA7s+WRU+HvKRrjF8BEQhFZSSY8mv88iW3O1Dbm6bGGLTuIS5v+xbRfV7XZY3vfeer5AGH+3k0CLaSUtVhNGnVpO4qrRXH97BHcv3YWX635h4WXvYsMHjsVty8eR1kZB8mPLrCpWjFZJWg6DGYWOZkhjyAsJMy8ljsiXveuMZFQp8cYqNn3qmu0lxflIebabmz95WvmyyfCJ0CQH5b2jqzhHXjnCEqyUqBs7sDCE/Njg5H+9DY0tPXQe8iYdn5UAt5roZDWO44ePYoLFy5ATk4OaWk10egKCgqQamU6rQBenCeMxoMjJ5Fy3AM6M3rzTGzl+USzy+BVzSe+CuDF49R5FOXlwXbXPIir1Yxni4gIQ21IJ0jqKOPZ14eR6xXJ1ofrDJjlqANaitiAYJTk5sOoubWbYV3YGnD+kxio9OfthlJBQ5CnIQl8bcWwhXOxY8EyyHU1hM6MXmxiUFhKHHmPwpFyxB29Pp4IbVNjzNm0DnuXrkTQzH+h0MOMpTIX+MSgLCMfo5Z93mxyNk0TPrl643kwCpdvwrLq0E7IuOjLnluBUNgy8nOzmbGuXq+JdSIhT8iM4xA2cads2Z2tQYTe2Icz+7dCWlYe8VHPIComygyMnXoN6pDiWmMs7R1gbGmHhPhYGA6eDXl9K7Z+W1FcgPh7R1lxMmzpt+3ys2ll+In7bcSGhzBDaEqyowCYlxVREcFPcevCcdY1r66qhOGgmXUiYZ2/p7E9VG174v7Vsxg2cRZbzWkJJALOXf4zLh7djdi7R+quJ3Nqmh4lLxoBAgS0HkE92n7oWppD18YCyQfcIGujxxPSRnYnKYfdYezQCRpGBm/1ON8VMhOSWICe/oJBbCOkFjlrXVis/QghX+xjK7tmP06o+xpNBRaVJLP/D7z3EEr9bXhEwloktZSYV2Duo/AmQiGF4NHX8zIy2/TxkMBHwXi5sVnQXzwEkroqEJUWByc9H8k770BBXR3dx41A93Ejkb0olQl68p0NIWmgCk5cJgoC4lloy/hvmvf9o1BCWmsuzy6E3tz+dSJhLTRhqDGhO/wP3sXYr79kXt4CXg7ZqshpGfMNtJDTMYOstgkgJAyrqd8j5uoubP11BcZMnYtAHw+Ul3GgrW+MXoNHQ01L951oXo+ZOp9t7ah37g81u17Mr5BbXYXcSF+keV9lTfn2CLsgcT4mPBh+j+6xYDvyIXfuN/Slvo75OVlM3KRQkeLCAlZDq3fi9fAUl1WEfr+pbCU84LEburr0a9ExUS07ee5SNm35xO0O0v1qbLLI75SCaaZ89hWPT6WA95N2FQq3b9/O/uzblzfpaN++fZg9e3Z7/uj3Hiq4RnzxGa5s2YnisBSoDLSFsKQ48jwjmIeedW8Xlr4loGX4XLkOpb5WdSJhQ8gLUsZCmyUS1wqFnJRccDLyoW5ogIrn6zQi8vw/bFAnmDxhqssqm3ytNDGb/dnWvimmjl0wZ9NvOPHLBkStOcPMqSs55RAREUGf6R9j5Jc14+JmTl2x8uxheJ6+iBAPT1RVFKNTD1e4Th7/wo5yWnQcKsvKIVJeAcVupnxXi+k6EipTn8TgQ4IMhQvzcplvSGt99fKyM1gXk9Yc+CEupwxRKVlUFOVCxcIJMprGuHflDETExSGjacJWd/0fb8DFo3ux5KdNLNmrI0Pvkc+/W4/t61Yi+uZ+FtZB6XIkEIqKimLOV6thZtP8+ntLKC0pZgWUnIJinXgaGeKPHb9/j+KifMioaDPB7+H1c1DV1MUXqzZCQ4f/8+924wKO7NgIaRUtyOpaoDApHIqGdnxvq2zWFZmBD5AUFwlji+bXpQjyC71x7gjuXz2Dgtya3wm0zmPv6MI6uqZW9oJkYAECXgNBPdq+fLz6W2yduxihi/5jjUNJfVWUxmYg66o/RKqEMGnz++WX3Z74XbvFNjzUhvKu+xEkgKmPcmDBHhRyUrsJUhiQwOxkaq2J5JqpR2tXwcsymqZ4V3EqWJNYVqlt/YAp2GXhjr9x+Ps1iNp8DcIULEI1cXklDDrZYua6nyAlV7OeunjPFvjfulcTChiUCQ01NYz6bRY6Dez7wnNgfEgoxDUVUFVeUTdp2RiFroZI2nsXWUnJH4xQWFlRgezMNFb7K6tpttqWJisjDZJqzU8CS6vpIT8uBGJSstDsOhgxN/dj/+a1kNU0goikNJ6FnMPN80cxac6XGDDq1UNx3hQ9B49GYX4uLh3fi+xQD0gpaqCsKBcVJYVw6j0YH81d+lr3T7Vefm4WRMUkIK9Y87mvtLgIO9Z/zzzJJeSUICYtB8+713D+8E58PP8rJrTyIzM1CRu/W4SSkhIomHRCcagnlMyaru0TFC5D4YDhQX4tEgpDnz7GlZP7ER1WE1YiJiHFJikdew2ChW0XQTLwB0S7rx4LaD8GzJkOVX1dliZHqb2EkrYmRn65AL2nTGqxN9aHTGV5OQLvPERBRhY0BzUvjEnqKKEsPZ/9P61NJP13DxKy0ijMzsGNXfvY9UFzd0C5tzU0J3SHtGG9+XPB01gWZkI+hQ2hYJSMcz4w7GwHFZ36iaS2wrqXC368cpp5L2YlJkFSVha2fXs2KQJpkpHWVejSUmoLNiFREVTklzR7u8r8EohLtj79710kNTEOF47sQoC3G/OAJJ9L267OGD1tfl3wxcuQkat5bcrys9iJvTGVnGJUlZVARFKG+fgVpkRC2cIRuq7j6iYQ6Xvjbu7H5p+WsTWDjj5ZSILq8t/+RVRoAPwfP3zehZ6I7n2GNOttk5YcD1/3OyguKoSahja69RkCGTlekT8pLgqXj/9X93rQpKJjz/5wGTAS2379BhIq2rAe+Tlb6yZKMpOQcO8oNq1egp+2HG7SKaWi7OiOP6Bq7QLdnuNYml9hckTz5urP1z5edh4kD6Adv3+HYD8vKFs4waTHeCZcUgebhENRMXE2eSlAgIBXR1CPti/aZiZYdnAXbu09iKdH7jDBRlRCnE2TDZo7s11qnPeRxJBnCHV7xMLzGk/F1UJBJGT5UlVUIxTmuD9DYWgieswfgN1ffI2yohIkH3iAvMdR0BjtAOU+1nXNXKo78/1i+a6IZ157isqSMjgMH9zmj4t8AT/fuRkpkdGI8nnKNlGMOts3aUhTYIvjiCHs0hpExMRQzq1GNacCVdQU5zNNSVY8xIdQk1ZUlOPayf0s7K64sOazC0310XotiWEt9Q2Xk1dAdn5Ws1+nepMEQaqxUryvQExGASbDPq3bsqBAk1Sf6zi1dzPUtXRh5+iCjgw9L8Mnz2bhH7TKm52RxmpUEgmba7xTGrSP2x2kJcXVhIc494Feo5q/jFPKPLmp2Vz7ehiYWWPYhBl4cOM8osNDYTRkDhQM6d+qMKv1U7yv4si/66GorMr3edv/z68o5wrBcvIKCImIIDvUs27dmO9jExYGl1uz9v8iyNJo39+/QFbDkPmk01RlYUoUQoMeIT0liW3fCPhwEChJ7zjUZaNLaWER61RIK8i/E0EG7UV2Ugo8Tp1j3nsEhXG4ThoHFd2mRWqkjx8OrlyN4pw8VpAVR6Y2+wGjOCIVYipyyLwRgMwrT5l/n4aRIa5s3QmlXpYwGGfPgkOybgUi1y2M+RwqdDFiHV/ylCF/wKx7wRBTkmHBIsXhyUg55I6S6AyM3Plduz0flKRM4mBbo21uClkVJVTTOrNnBN9AFBIQc93DMeiTGXjfSY6LxsbvPgfEJKHjMgZSKjrg5KYjOtgNG1cuwLI1/8DY8sVTZQQl5hpZ2CIz6CFLAm6YvEaQOEWQsXCS2xlIKKhBv89HrACohYQvWuMNPb4OT9xuvxP+hVSc0eTgy6YHqTt++N/1rIATlZCCuIw8SvOyWFoddazJTJugFQ4KGRGRkmd+LRKK6ixN2N/XA74e9yAkKgajoZ9CRKx+JVhaTZddF3rsN3g/uIneQ2vuqxYy7aafqdODksSFWcgMt7ICBYlhUDCwaXKseTGBEJeUeulUJ/2soCcezJdRQZ/CgGpQNLJDRsAD3L5wDE69BsDAtP5rAgQIENDRUDPQw9Q132PS98vBKSqGlJwsE34+VCg0hKYD/a7fQnFBIVR1deA8bhQsXbo1EWqK8wtw4JsfEfXYl20HUcoxBemRxU1jKJSEmrRUl9JaNwXX6Via4+au/ZAx0axZvRUXQe6jCMSsv4jCoEQYLB7CfmbqCU9U5pWg+FkK8p/GQp6CRfKKkXHFD2knPOE6aSxLcm5PQZkubY2Naw94XLgIbiWFCAbzeGbXknn1KVT0daBhbIj3GaqTtq39GhHB/iyUQ8vIDtWVFSy5l9ZqM1ITMWH24hbdl3PfITi6809Wz0oq1Vgl1cLJy0B+fCh0XceiICGM3cZ83BIeKxZKP9Z2HoWSjATcOHukwwuFDW1ghk2a9dLbkX3Noa3rUVZWCilFdVRyinD15H7YOfXEp1/9yBrO1PymejQxNpLZBmnqWaKyrBS54d7Y8XuNtY7hoFms5q+FwuzIgqg8LwNXTx1o8rzRZw6a9jMaPAtiMvLscyrVueT1XRs40/i1ok0dE0v7Fz4emm48/O8GKJs5QL/fx3XCo5yuOZTNHRF5/h9cOfEfPprXvA2AgPcLgVD4nkAF2YdOwO37OPzdzxCSFINCt5pCxP3cBWbsPP3XH9FpUP24dVp0LPZ8uQLSVtqw/X0y8n1jkLTnLkqi0+t8AxumrXGSctilMCAeZt0doGDZCX437sDi96lsNbkWSnSLXHMG0WvPQWO8E7JvBgEllSzFzvviNWRe8qu7rbKuNuZt2QDjrk3XSzo6JED2nf4RLm/eAWFpcUSsOgnj5aMgZVAznVWakIWYjZcgKSUFl4nvv9ktraMKScnBbPRiiDxPRJPVMmYn2+grO3Bw6zqs3nK4RV3csdPn4+/VS1kasKbTMDZZSIEWmUHuSH96Bxpd+rM1DyrMNLoO5BEJG4qF5CsT5PuoXYVCKkjDg/1QUlQIVQ1tGJpZtWvC9bGdf8L74S3o9Z4IZXMnZqxcUVqINJ8bOLbzDzZV6ODaHwf++Q0SSpowGbEAwmI1H1Tl9SygYtUdwYfWQNW0K49IWIuEvAoLUfHzfNBEKEyIDoeMtikrfAlaD5fWMECSx3lm7E1r4bUUpcYgK+gB+gwZ81IPF+r4y+tZ8oiEtZBHTlbwQzy8fgEzFguEQgECBHR8xCQk2OVDJj8jC/8uWILMuEQodDWCuIkCoimsY/EDdB7SH9PWrqrb+qEP+f8tW4mk6GiY/jAeMhZaCJy9HamnvKA3h3dNkES99PNPmCAW/dt5KGppoB8F6h08Bo2xTtCbP6DuHKw+0oE1t+P+vgohUWGUJeYwcZA8zmP9AxHx3fG6+xWTlED/2dMw7POWb5Z0JFw/Gg+P0+dZQz9h9x3mt6jc24oFEFYVlyHlpCdyPcIx5efv3vthikd3ryA80JcFkJCPYC0KBtbIUNdngReOPQfCwNTypffVve9Q3Ll8itWx2s5joEANbAghLy4IyY8uslqTNiFSPC9BQkEdMhpNfUjp/ahk7oioByfZZF17brkkxUayjRMJSWlY2HWFuET7TY+GB/li758/Q9GkE0ydR0JcVokJ/LkxAQh1O409f/6MxT9swN1LJ1n9aDp6Ec/zo2TaBRHnNqMsP5NHJGz4vClbOSP2zhEU5OVAXrG+xkyICWd/yutb191WzbYXktzPIifClwWP1EKiZOL9E5BTVEFX1xevHVN9TZt22t1HNJlOlFRUh4pVDzy6exXjZ30OMfEP+3f8h4JAKBTwXpARn8BEQgUXcxguHQ4RSbE6z5W4zVdx6Ls10DI3gbpBjfcYpRSLKEixVDiaJiRvQvIgfLbyKLQmObOUNPJPoU5t+nkf2A3og9HLPoekjAwkZWXw89DxUB1ixyMSEnRfBosGs1S51GOecBg+CAM/ncF+7vDF8/HM4zFKCwrZhKOJY5d3qmCpKCtjYmzk4yeorq6GgZ0Nuo0ZDu8LV1Eal4ngBbtZih3BScyGnKoy5v37V7Mpde8LKYmxiHkWxDqCtSJhLSRSaToORdTlHYh+FsS85l6GhZ0DFq5ch8PbNzLzYSERUXCrKtmfmg4Doek4hH2woPVUfmJXLUKiEqisaOqL2VZQUAf5uBQX5NVdp61vgqkLl7focbaWnMx0PLpzBdo9RrP131rEpOSg22sCKorzcPnEPlZMpSfHw3TU53UiYcMuLa1oiEo231ghD8iy576jjVebqiuK6/5OhZnhgBmIuvQvQo//DkUje0goqKAkIxEFic9gZtMFY2cseOnjSk9OgKJtb75fIxFYWtMYqUk1qaECBAgQIKDjc2DljygozIPt9rl1DVQ6b1PjOWDDJWgaG2Hw/BpvzEhvX8Q+DYL5rx8zUZHQmdmbbaOUZxVCfURXiCnLoDAwgU39SYqIY87BP6GgpsoulOQrJi8N3U/6NmnUUSAfrRRnXPKDno0lxm5Yw5rmdCxxAcFIj42HhLQULF26v1MDB3T8CSFh8L1yE8V5+VDS1MC4FUtxbuNmJqLGbLiIhF23WcAJ1aPcymqM+HIBnEYPw/sONRblDWx4RELe5qMb3G9dbJFQSKIeJRnv3bQGEbcPPl9fpRegmqUeGw6YxupQWpUVfoGHZG2tWllZAQm0vVBIVjOHtq1HfGRo3XWS0rIYMm4qhk6c2S4N7CsnD0BaXQ8G/afVNeypviR/avp5wbcPMYGQmsGKZl2biKh0G/J4rCwtarI9VIuoVI39Dk0l8lwv+vwzbkUZa5gTqjY92ORm/N0jyAx2Z1OAlaWFyI/xh6iIKPMtF2tUEzeGamcpJXW2Qs4Pes1pYCE/N4dtQAl4/xEIhQLeCzxOnoewjASMvhrBEolrIcHQaNkIBPrHw+PEOYxbsYQVGAG37kF9Urc6Dxi6ncVvU5C46w6SD7vVrAvTiUZOBgNmT8OQBXPqur+5qekoys6FltMgvsciqa0MKT1VqCtqMD+Ye/uPwrZfL1i5OrM18XeRlIgo7Pria+blKGumDSExYfhduw0JGSlM+PYrJD2LRHxQCCrLyqCqpwuHzz5Hp0F9P4i1o4zkRPanrDb/FVOWCke3S0lssYBm360n1jk4I8TvMeKjwlihUZSfy9Znk9zOoiQjnnn85MeHsMKvMVXlHBSnRsOgxzS0FcnxMawzSl56FBBSzimFrK4ZLAZ/yibxitPjke57E3//uAT/+3UbjMxrOp2vCwWRUJFEaXBUjNFUYGOo4FKxcUXM1d0slZic0pt7PSQVNZiQp+nQ9N8vpdsVp0TBxoU3NY6gYJGw3X+jvCiXdY4JCXllWExYhqwQT6Q/vcUSk3UNTTH2i+/QrffgFgWQSEhJo7K4xrOGH5WlBZBUrxHgO+wHtuhnSE9OhJSMLBO6W5ryLECAAAHvo89g3NMgZkFTKxLWnaf6WKMoJBFuJ86g/+yprEYKuHUfUjoqLI24Fq3JPSAiK4nU44+Qcy+k9g5g5dqdCWIN14MTn4VDrosBT+3bEMUe5ihPyIWWiRGC7rshNy0dTqOHw6izHbu8a1DT+vD3vyDozgNIqCuwRONST09U5BezDRZFTQ2EunuiIDMbslIKMJ85BC6TxkJRndcn/H2Fak3Vrv9v7y7Aqr67OIB/pbu7G8UWuztmt2s350qnmwt15TZd73VzzpoLN50xuwO7O1HpEOnuhvc5B0HiouBAQM7nee7jdi9e/tyrcji/E4p/PqGElKa5A6LC71T5+Whxxaz5P3MM6Od9GRdOHOTD8fysNERd8uKOF4pFSU5aEm/ZLY8eNza3gpa24rnT1UUx6IkD23H60B4kJcRxPKqsrgWH/s/z5l+6pribp3lueEZ6GsZOnlYjnzcnOxtpKUkoRAH8blyCXe9JCrt6DJxaQU1Ll1+rxLho2Lfqp/D5qGMo7uYpZKckcDxZHi3Mo4SnvmHZGNC9VXv+mTTB9zzM2/Tl+6gCkNqFqeoz/MwOxFw7DGNTS/QfNh69hozhduqH0dDUQm5mGldGUtKzPHpdiz6u/s4+T06MR8Dt6zw309GtOYzNFC83ElUjiULxRPA7fxEGXd0UBkp0Hz3me+4C/z/945Gblc0tCqWp6GhwotH2lT7cSmuubYpXl/yPN7aV+Ti1eyc5GdmV/uCcm5qB0PBbSEAq8lMycW7bbq5ofHXxD9A3M3nk4PP05u2ICgqGmqYmWvftBc+hA6GupYXalJGSimVvzAIM1dHiy1ehaVP0DSsnNgUhP+3Fjp+W4v0Nq2p1rk19pqld1Fqam57M1W3l5aQll3wDrg5KOtGWMboNGPUMzh/bj/MnDiEjLQY2Li4w7d4LXtvWVmgzoD/f4ae2cdKLhlbXhCtnj2Hl959yy7O+c1toqqohOfgG0u768dINy07DUJCbDSgpI7+gAP/7+C30GDgcfYaO4wHWj+Lm5bM8m6Vk65qaOpRU1SutoqSBy6To5LiQT2mL7yuN5glGXtiDxMBrMHQu2/YfffUIstOS0GPwKIUtOLs3rELI/j9hP+BFTo7e+4TISozgbdUffL20whDrh2nftQ+OHdgJyw5DKlSk0syf1PAAtB89HvVRiP9trF76LcKD/Uvu09TWxZDxL2DAyKdrtQ1dCCHqI/+Ll6GipQ6DDopn8Rn1bs4VflFBIbBp6obsjAyuGCz/76XZU225IjBi/SmeRTh782qY39tsXL7aPT/jfrV7edR6m5uTg5s3LkFZSx1XvQ5j79Lf8Mz8jx/58DotIZHj2tunz6EgPx/2LTw4SUdzKmvblm9/ws0Tp+A0eySMejTl9mLqAIrZdRmnV27HsLffwIw/l6Kx0tDS5ni0MnQwqWlU/Xid5i3TrfdTYzlhSJWLkeGh0NfUQr/nX+cKO2p9dRjwApSU7/8slnznNpICr2Hsi2/WSExAiaD/fTQdcdER0HdsBcPmzZAZH8ExadRlL+hQJWVhIXfiUEzltWM9QgJuc0xCs/4e5RoS42J4Od654weQl0M/+xU9B80HrCwhS9WAdMhNr0Vxgq08fec2wIlNiDiznV+30pWFmQmRSLh9Fr0GjajQ5ksbk2kx36lDuzkWpdeBv67CQi4UyE1LwsDRT2P0829U6+ts26U3x93UPk2VkaXRzxbxt05zt4yOXs1uRq8JWZkZWP/rQp77XVCQX5I8pcKL56fNrpfX3BBIolA8EegfsCbKlf/jT4FE8dZDJSoNt7FE6o07HIRV+Fg1VeREJsFhXN8KSUJCrbTWzdwQ73WjaAZKuW86KVdCeFi062fjSjbLpd2+i6CvtuPXGe/h3bV/VKvlmK57zy+/4tAfa/j0VKelLfKSErDpm4U4uGoN3lj2E0ztHi0ZUxUXduxBRkoKWi16A2om9xNh1K7t/Mlo3Ji8HCfWb8bo92egMaLhwLr6Rlzqb997UoXH426ehJqGFjzaVqyEqypKMtLMvNJz8+jPBZ1snjn8DxL9LkLP3oPbEJL8LyErKRaTZ34MQ+P/foKenBCH3//3GfQcmnOLRXEAaOE5ELHeJzgpmREXjrRwf2hbOMKiXX9uQzl5aC9Oeu3kGS1UZVYdpw7uwupfvoaOpSPs+jzNLcN06kpzGTMToqBpVPGEMC0igK+tfc/+2Lt5NZ8mW3YYXOHjslJieZNw6MG/kRzcBvqOLTmgTAy4jJQ7PrzxTtGWak0tbcz8bCEWff4uLzyhGZDUDk7zCJWaNMEr736mMEkY4ncbW1YvRViQP79nphZWGDbxJbTq2IP/7egzbDxOHtyFoL0rYdNjHA8Cp4+jLXNhh/7h1p8TB3bi6tnj6NCjPwdyValUrG00C+h/H0+Hmr4ZL2KhmZwUnNLfgy2rliA7MxPDn55S15cphBCPVWFBIR8g8a2SeLTk4+4tgbl+7DgfPlMir/zH5salQdfMGGYORaNzymveoysCFv7CbcqlYzRSkEvLPW7AoKMLXD8dW7L9986yA1g95zMY/bWcW5Krg+YbrpzxAXKysqHXwYkP40/v2Inj6zZi/Efvo/PoYajN2Y8XduyF9ZTeMO59v2uBrsFiTEdkhSfgyN/r0fOZ8fXi+2RdaN+9L04c3A1Lz4EVDh85VosMQvtJj75kkOIWiunKx3WWdk5Y/s2H8NnwLc8kpBEvVBGXEnoTLdp344PjmkAtxknJyXAf/x7PzSt9sOq/YwmC9/+BjNi7XGFo3LQTHzCH37mFpV9+wNcw4ZW3q5UsjIuOxHdzXkdWdg5MW/fhduGspGhEnN3NcSfNmC4vNz0FmYnRsLR1QJtOPXH79nmYtuxZJoFKMmPvAgUFXHHpt+UnnklIB9z0vFwtaGmDoRNeUnhdE6bMQHJiHK4f+AuaBqZQ1TdFdmIUVyd27DUII56ZWuH30Dzxnet+w6VTh5Gdnc1xbZe+QzBozLM8T5tiWFrEcuvERj5wN3Rqw5WFVCl699RWpEffQaaWGsd+7i3aofvA4TAwqvtKXVrm+sv89xAc4AvLzsNgyO3fSrxU8NaFvVy8MPvbFdUu2BCSKBRPCKc2rXDl+DEUvjagJAgrVphfgOQz/mjb8347YdexI7Fn6UqkDfeEjnvZjciRG04jNy2Lt9NVpt9Lz+HvDz5F+F/HYfV015IW5jSfcF7ioe1uBf2OLiUfr9PMBo5zRsLnvdXwPX0ezbp3rvLXdmn3fk4S2rzchwOh4q8vKyIBAfM24be3Z2P2xr85AVobbhw9Cf0OzhUCUKKsoQbD3s1w4+jxRpsopGB06MTJfJJFgZF5mz6c2MrPzuSkScy1oxj+9CsP/QZFQ54vnPCC743LnChybtoSnXsP5nZORSjQeeGtD9G0VXsc2bMZd87u5FaElp5d0X/kRDi5P3zLclVQso9+nLHtOaFMkEOf36xlT8ReP8FJQkoilq5stOz4FFffLf/mI3z925Yqf4Om5Ofa5T9wgGfba3yZrWveqz/nlgrnwVPKtEVQEBN7/RjadekFc0tb9Bs2Hge2reUg2aRZF55VSO9HzPWjSPS9gPFTZvL1H961CSFef/Nz2Dq7Y/y7n3MyrjI2jq5YsHwDv0/UFk6VFI69eqNr/+F8wlve1tXLedMfz/OxduPWkpjIICz7ei6fbL8x9xtui3j785/4Pp+NP3DAl5+Xy18T0dA3QVKBKmLvRuD6ws/4GmbM+1Hh53uctv3zK1S09OE8/I2SKk9lQ3PY9hjLr/veTX/zFurSA7iFEOJJ59i6BfLSs5ByNaRk5mBpiSd9eKxNcXUgzXo+sHIVwv85yRuLSycxaMMxtR73f+n5SpMbHYYPhtfvqxHwxWY4fzQK6uZFlTN5KRkIXrSXtyfTzMNiqgZacHx/OG4F/o6jazbg+a/nVflro+3MK2fOhqqDMZp+OJqfi1BF350VB/Hv/O9g4eQAh9Y1E3+U50MVjAUFMBmoeIwLHf7f2nMFd7xvw6ltzc9Kbgj6DpvALbk0isW6xxhomdigsLAAqWG+uHt8IyxsHdG264MrSSkGDbh1DeePH+DxL0amFujafyisbCv+eS5GMc3cH37Dwe3rceXsceTmZMPS1hHDXn+ffy91yfxXVEXofek07HpNLJMkJLSV2axNH0Sc3cWdI6UrGy3bD+LD4yO7N8G5WUte5lJVG39fhOz8AriNnVVSQUjtzdnJ8Yj1PgUj945lroVe64hzu6GiosIjaKj99dqc1xBy4C9YdxvFFYD0MXQwHXZsA+xdPTBu8jTs3/oPvE9u4apALV199B8+HoPHPF/pzwBUZUgxJL1PZ4/s5YUnhs3c0KXvUIWjf0IDfPD9h2/wshJavqdr34IrHSlWO7Z3K+Z8v5I7gKbMmoc/fvwC1w/9g4jT26GqqY2MhBguoqR5iKnQQGp6HoI2r8G+LWvw2gcL6nyb9dVzx/l1cBnxJnSt7v/sbeLRhQsYfDf+gDOHdvPBvKgeSRSKJ0L3iWNwbtsu3FnhBbtSyUJKEtJ92fGp6DZhzP2PnzSOZ7X4zVkHkyGtYdDBBXkZ2Yg/cB1J5wN48ciDWmnbDOiDhJmvY9fPKxC35wq0mlohLyEd6YFRUDPV5ZPb8kGdjoc1tGxN4H30ZJUThfTN+sjq9XwaTEtWys9CdHh3KG6/8zdunzqL5j27oTbkZmdBxajyzWEquhpIz85BY0bzP7Iy0nm5R9yN41DXNUQ2tRwX5GPQmOcwZPyLD/z9NIdw8fz3OUmmY+7AlQiXTh7G9n9W4o25X1VakUd/xjr1HsS32hLk683bflXKnUwXK8zPhb5DizJJQkLJI9teE3Fz7QJcOO6FHoOqtv36zOG9/OfeqvOwMlvXKOCz7/M0gvb9Dp/NC2HWsgcHOzQbMfbGceTnZiEsOAAHd6zn15zaEI7t28pzE2mmYHZKHM9dGTZpCvoOowRkEz5dpiCYKny1dKo2O4cq/LoPGMG3B6EKwP1b1nD7icuw13gzMr9ehYVcvXjj8FoO0IZOfIm3RX/16yZcPX8Cwb43eQA2zVqkRKlx044lrwMtSgne9zt++988nhlUV+jPqfelM0VJQQWt4GateiH22lGez9Nv+IQ6uUYhhKgLjm1bwcrdBWHLvKD51STuvihGycOYHZfQ65kJJR0rtIhjxNtvYvv/fkFWaBxMBrXmuCr5UhDi9lyFhbMTej8/sdLPp6mri9eX/g8rpr+H6y8t5yV7TVSVefkJddvQJmUth7JVP0oqyjDq3wLeG05U62u7sH0PcjIz0XTuqJIkIT+fmgrspw1C2vUwHPvn31pLFNJ8Qorvy1deFlPWLXpN8xpxTEpLJujwccW3H8F300Ko6xpxnJaTkQoHVw+8PverBy61oEPrX7/7mMe/0EElxVmZVy/i4PZ16DdiIsa99FalSWua0UzdLJNn1s7XRvERJdKoE0SR/Jzsoll9vcoebBOT5l2RFHSND4irmihMSojF9QunYNNjTIU2Y+pYoS4X380/wrRFd14eUzwbMT06hGPKbWuWo/fQcXh9ztecfKNuFC1Dc+TnZPLPCM7NWuH1OV9BV98Qrs3b8GtPy/R0dPWqVPxB7wP9Pro9CMXDCz95i5OENt3H8GtRHFdmp8TzwsUfP5mBL3/dxIf6b374DS+IodngSfGxOHt0P3RtmxZ1+Nz7WYAO30OPrOM/Z/N+Xg3TRxwzVBPOHN7DHUilk4TFqAOJ5jaeOrxHEoWPQBKF4olg5eaCcR+9h40LfkDK+SAYdHfn+5NO+iIrJhnjP3oP1u73/wFR01DH68t+xIFfV+Hslp2I3lo0v9DC1QnPfvUpPIcoHgRcWt/Jz6JVv944u3UnYoJDoWygimuBUbCe3BuqRjoK/0GnIKZovkXVpCcmIdIvEE5zFCdZqHJR08oIfucu1Vqi0NrNtahaM5/auyu2TKdcDIa1W8V/nBuCnNxsHDm3H4fP7kMstbRq6KBrmx4Y1GMkTAwfPvi39HtLm9W6DRh+b3hxDFdSdejZ/6Fl+anJiVj02SwoaRvBY+ibJbPvaMbMnSPr8MuCD/ibsIl52crXx4Xb5O/N+yiPWp0pMKLBzYqo6RpCx8wOgT7Xq5wojAwL4QHPVJVZnr69ByzbD0bkhX24c+zfojubNOFEIJ3q5qQnYvOqpTh5YCdmLfgF/UdO4nkl9Bobmpijc+9BPJi79Pumo6d4u9t/Re0dFNDa9RxXkiQs/pxGrp5IjwyG1/aipCZVpVI1qGfXPmjTsQfmTh3DCUKTZmUPB2jDnnX30fA78BfuBPkpbJF+HFKSE/lr0zBUvPWO3jsaaJ6SGP/Yr00IIeoS/Rv/4nfzsfTVGbjxygoY9mjKY2PSfSKQfCUYbp07YPDrL5f5Pb2emwh9czMc/P1vBH61le/T0NNBtzGjeJnew2ZRW7u74qMd63Fl70H4nr3A1e4xtgXI0G8Cwy6Kv09QMjIvJ5cPr6raiulz9gJ02zpA1bDi9+cmSk1g2Lsp/HZcRG3G+rTVOPVqaJnlL8VSLgVxnGruVPGxhsDn+iUc2rEBfjev8Pvi6tGaD9uqO7qGDh8XrPgXNy6cRrD/TSgrKcOjXWfuVHnYe71m6bfwuXEFjoNe4kNg+viC/DzEeZ/kazM0NuPYqi4ULw4pLMhT+Dht/dW2dIKKpuIqPEowBtMM7yr+maclbVT9R9t+FcU5bmPewc3V8xDvfYK3ARNlNQ0YurTjxOLZk0d4lM7U9xfg2z+24eLJQwgL9udEbetOPbjzp/R10EE03WoaxcGUgNR3asVJzdLoZw6Hvs/Cb9vPnBwurg6kpC/dNq/6hbtyHPo9x78Wo84Ruu/WP/O5InHcy2+hrtBCG3XDypeWaBhaIDnw0mO9pieFJArFE6PLmBGwaeqOE+s3IeA0bT4FWnTohB6TximcwaKuqYnhM9/AkDemIDEqhpeUGJibVWt2BVUdDpvxOv83feOZf/MWUq+GwKRvxdNUmguT7h8Jy8FVS5gQarEgSqqK/6rStTZRUeagsLbQtjhKpkasOwWrZ7uXeX3ivK5zu3X3hTWzUexxysrOxDcrP0FQmD8HD2bO7Xij7aELB3H80mF8+OqXsLdWvDm3MnQqSNVq1XHKayeysjLhMfZlXhZSTFVbHw6DXsLtfxbg6J4tGPfSdNSFpq07wPvPX4qWtWgrTqrRjL/KUBWfklLVv9XQ30taRFJZIEdzZ6gFwqNNJ/jd9ub5eNpmdmXm1ATuXIY1S7/Bmx9+yxV7jxttxgsPDeTZOHp2irc/0wyVuFunEXU3hNuJi0XeDeEEm2sPxYE4Be7Kauq4ffVCnSUK9fQN+TQ6KyGST3HLoxmV1DqtV25TnxBCNAY0N/q99X/izOYduLTPC6k3ImFsbYWnvvgI7Qb3h7KCmI46VWi5SHJMLHKzc2BgbgrVamyQp++dnccM5xuhhSVH/tmA/MwcKGtWrCBLuRQMCxfHasW8tCSNYs7KUKxam/EobWk2d3bA3T+Owu3rSbyEsFh2dDKiNpxFi17dH3lpYF2icSk035cOSo1a9uZD0JCga/j581kY9dxrfBhdHdTq26ZzT75VZx4fHXZT1ZlBqao9qs4za92b4yu6TurGoMPNx82tRVu+lkS/S3w95eWmJ0FZrfJEG8WqirYUV6Z4uy/FpCjX6kzouShW7dJnME4c2AGL9oNh4Tmg5O8UjeAJPbSGu0Coa6Rrv6GoC9cvnOQROOUXlBTTMrfn+N73xqUKbcTel89B36FlmSRhMbpPz7ElvK+cwzjUXaLQwMgESeFRlT6elRAFfSOJRx+FJArFE8XWwx3PfPFRtX6PippajSwDoW8M3cePxu4lK2HcvyX0Wt3fTkfVeGErD0JJSRkdhg+p8nPqGBnC0MoCiad9Ydi1YlIgMyweGXdi4fBac9QW2so3ZNpU7F2ykltZjHp7QElVGYknfblNm2Y5Nu9d9oSqIdi0fw1CIoLhOnI6tM3vv1f0TT5o1wr8vPprfP/BimotnimNWlqjw+9ATV0dVvbOlT7P1fMnoWffvEySsBi1deo7teaW1LpKFHbpMwS7N/yJkIN/w2Hg5JLNznTCHHluNwez8X4XYVyu+o3Q4pH0uLs4cyQSt66eR9e+Q7j0nxKqlWnXpTeO7NqIlDAf6Ns1q/BDSoLPWQ4WKVFG7SClk4TFc2rMOwzG9eMbOeilNpyqSktJxunDu3Hj4mnk5ebBwbUpeg4axQOpq6N449oDB9rf225XcG/JUsnvvfdDFi1KqeQ3cuKVXv+6QtvjWnh2QYD3cRi6eVZoP465dox/7dDj4ZXZQgjxJNI20Ef/Kc/zrTpxJB1Y1wSKzQ7+sRp3/zgCuzcHlkkIJp3z59u4D9+r1nM6tGyB4LXrFS5e4ec95Qf7VrUXj9LX8NyXn2LJqzNw643fYTy4FY/hoVmO8QduQFdfH2Nmv4OGhubHUZLQvG0/Ti4Vv1fmbfoi6uJ+bFuzAm4t2sGp6aO1dOfn5yE8JBB5ebmwtHGodO4dVZTRIaCRW3uFj9PsaD+fc9zRoGgOXm2jTp1OvQbi/Mn9vPiNZlcXo5gxKzGGux1yUhO4Zbo0Sugl+F/iCsG3nxmI5m07P3Set62jGwxNLXiZHi1sKy/+9hn+uS78TjAfmlq2H1jmcUpq2vaagJurv+CigIeNISofC964dJrH8VDFnL6hMbr0fQqt2net9kz6/PyiopMmTZQrLzpRUiopTil/HU1UKl8MpKSsivxaPByoii79hsL7u4+RGh4AXeuy1Z+8ETvEG4NeqaV++CecJAqFqEG0aY3aPvw+2gCjnk2h5+mEvJRMDmAy78TiuQWfcPBYVZRc6jFxLHYuWsbtI4bdilqqSX56NkIX7YWOsSGfQtemAa+8AEtnRxxZsx7Bi/fxfTSDZ/Bnc9BhxP2gpqHIysnC0fMHYNKiR5kkIVFR14JVt1Hw37YY3v5X0Mq9eht7aaAwlepTiwFt4iJGZpYYMvZ5dB84osJrlZebA2WNyufjUQVdds79eTuBPjfgtW0dB3SUkLJ3acanu+2796uV94HmrEz/5Hss/uI93Fozn+eUUKVc2l1f5GVloGnLdtwuE3lxP288Lk6AUXVmiNdf/LHULpydHIv929fjzJF9eP/rpTwcWxEXj9Zw8WiD0CNrgV4TeZszBa5UoUaLTDITIuE+8Cn43bgMfUfFLc/UCh127F8E+92scqIwxP82n9zTLJfir/Hk4X08/HrS1HfQ+6mijZFVQa0j9PUlxEbx9jqaXVMebWPjqrzMjDL3m1vbQV1TiwOb8n82SVpkIHKz0nlAdl0a+dyrvAkwcMdSmLcfCB1LZ36PqD2JKiVp43FdL1wRQojGytDSHOPmvouNC75Hhm8UjAa0gLKWGpLPB/JBb/Ne3XmRSnV0Hjsch//6B6GL98Hh3WE867A4CRO95TzS/CLQ8+fa/YGc2qxnrfkNh//8B5c2HUBuVjY09fXQY+wo9HnhaT5gb2iO7tnMy84sOwwpE8fRf1u0H4ikgMv8MdVNFNL7cnjnvziwbR2SE2L5PhU1dXTuNRCjX3wT2jp6FeJRiuGUVBTPMCzeokwfR1KSEnF45wacPrwXqckJ0Dc0Qbf+Q9Fn6PhaG+sy6dVZiI+Jgt+u5XxQrG5oztt+02PCYOvcFNF3QxHs9TecBk+Bqlapg+3ze5AZFw7jZl14NMrt25dw5cxRvDjjQ3Tuo7iAgxJywyZMxuol30BVx5ATuTSjj56PthJHnt+LnoNGcnuxefvBCp+Dfqag1uUAnxtV/hpzsrOw9Ks58Ll2AdpmttxWGxscjGtfz4FbS09M++jbarUou7dsi9vXL3Dcqe9QMXakZFpOaiLPIyzPyb05Ll84g8KuoypUY9IM1JQ7N9HWs3qt8TWtTacecG3eFkH7f4e55yAYubSjH6CRHHQdURf3wdLOsc6qORs6SRQKUYOoOnHqz9/hxPrNOLVxK4IP3+R/WJv16IK+n3z6SFvYejwzDiE3vHF9wRbotbaHbhsH5CamIfHILSgVKuG1X37gz1vbWvTpwTcahltQUMhzHhuq6NgIZGdnKvyGSbTNHbjCj9qSq5MopIq07+a8geSUZJi3HwI9W3duw4y/fRb/LPsOyYnxGDap7Gwiah+9RN+ECwoqfhMuLERq2G0432sxPX1oN/7+5WtoGprDpG1/3kAWF3oLv/9vHi+/eOb192olWUgnrvOXrefPf+PiGeTlZaN59z48q44SloROvWmIs55tU+RlpfHpLm2Bdhs9o2RGn3m7fgjYsRSrfv6q0mUcdP1vzP2aByT77fsD6jr6UNbQRmZ8FNTUNfDKe1/cr86sZHYitTuTqlaDZmWmY/H894rmRI6ZBVWtouCZgkHaoEcbramqsLKlMoq+hkFjn8O6FQsRdnwTXEe8WaZtOzXcH7HeJ6CipYPl33yIBcs2lCxTKVqWMhxH927lJKmOxf3W3tzMNESc2gZza3u4t6peArum0eyadxcsxpql3yFo7+8l99O2PppV02945cP3hRBC1L4uY0fA2MYKR/5eB99lXlxtZepoh1Hvz0DXcSOr3T5Ki1eeXfAJ1nz4BdJuhcOwVzMoqasi+Yw/L/Pr9/Jz8OhR+xtQaezPhE8/wLiP3uUFJ6oaGo/c/VEfBPvfhq5tM4VtsXSgqGvfHEF+t6r9vBv/+JkThUZNO8K1x0Q+AKUFHGdPHOLn++CbZdDQvD9vksagFOTl8IGkosUQ9HupSs7Cxp47Nr7/8E2kp6XCwNUT1i3MkRUfiX1b1+LcsQN476slD53R/SgoRqJlLdcvUrXdHq62s3V0hE6rNvC+fAY52ZnIib0L7zWfQ9/Og2cGJofeQn52Bqy7juRla4SSfmHHN3JMTUkmYzPFh9c0ezw1JQk7/lnJB6GaBqZ8KEoHtp37PoUJU2bi9OE99ztJFKCZitX58/nv74vgf+sanIe+yjF1sZQwXwQeWIUNv/2EF6bPrfLzdes/DDvWrkSC/0X+uaf0XHH6GYVmflPLNi0vodbzDj3uL3uhQ3J6nSPO7YJV5+ElP2PQzycR53cjOyWhWgfptYHa7Kd9/B2/LueO7kHEmR0lf3eo9f7ZNz6oldmPjYEkCoWoYZS0o1PN3s9P4pNOCsQUzaOpKvr9L3zzOa4eOIJTm7YiavsVqGtpouvIEejx9FgYWVW9tbImPI6kZG1TureUhQIihQoKeJaJSmXtn5WgLbdJifE84Fhd//6MHKooU9c3xa4Nf/DJZekqN9qYTAm4qMtesPAs2x5E7Q4ZceHoPe1dDsroVJNaP2x7jivZWGbWsififc7hxP4NaNrKE57d+qKmUEXktfMncOXMMWRlZXDLyvPT50BLWxffzn4NSclJfIpKgUx69B1EXz3MrR1AIQycWsOu5/iSE2hCS0csOgyB36E1iLobysGmItq6enhn/s9cEUjbg+l01creCR17DuCglpaT0N+LBP/LMG/Tp8LvT/S/xMHswzbBFTt39AAyUlPgMeKtkiQhoeegwDI9MpAXj1Q1UUh6DBwJ70tnuY355tovYeDchrcP0pbm1Lu+3DJj23M8bq//BmeP7isz23LEM1O5wjFg+y88k1DLzJ5baZICL0NDQxOvzf62XlTxUjXrRwv/5C3NMZFh/N64t2wHVbWqHyLcvHKOqzaD/W7x6X2Ltp34tbCto/mLQgjxJHHr1J5vebm5KMjLh6qG+n/6/tFmYF+Y2tvhxLqNuH30HD+nXYtm6DHrPTTt+ngri+h7xsMWvTQE9HVUGo9SSJqbDZVqJnVpay0lCa27joJZq/tzCmkGIiWL/Lb8hCO7NpVph6WxLuY29og8uxOaQ18v2XBLspJiEXPtCMeYND7m1++nIzuvAO7j3+cKvWJmbfogYMcS/LP0e07e1CRqnz55cCfiosKhpaOHHgNH8KIX6uKhRSu01I7mO1N7cfSVw3xg3eReZ47rhA94A24xSspadxuF5OBrOHFgO8+BrMzgsc9z2++5o/sQHxsFHV19dOg5ABbWRTFs01btERh4hVvFy//dovneaRGBaDa4atW7aSlJnJijyrjSSUJCxQfm7Qfh7JE9fL3Uil0V9H7RITttsw4+sAraFo4lW5oTA67wqBuX4a8j8sJenkFZOlFo79IU41+ewUnnVBoJ5NSa708OusbjhWgLNi3PqWu0rfnFtz7E6OdfR+Dt6zzWx9HVA0am5lV+Dvo56+juTbh4+ghysrJgaWuPnoNHoUP3/tVu935SSKJQiFpC3yzUNO8PWv4v6B+odkP68038d9ZmtjA0MEGC70XFbaEh3sjLyUJLN8WDfxWh2R7UfmDo3rFMkrB08BTnfQJnDu/G8KdfKfNNmBJDdNqXHu4Pfee2HMAkB1/n00Oa6efRpiO2r1nBs+AocVWcJCxGycNEv4s4sntzjSUKkxPisOjzWYgIDeTWB2qP9rlxlYMIV482SEyIg9uYtzkBSmhejIlHZ275SAq8Cptuo8skCYsZOLRA6L1W38oShcV/f6iSUdH8GAp6OvcegrPHDnDQW3pODVXrRV3aj/bd+uLauRO4deUcz09xcPPgU1Wa81Le7WvneVNebkYqMmLv8hxGLXM7fp15ZpRLW/hcOlCt149Oj6kycsak/tAwtUNmbBjS7vpBTc8Ydn2e5taIJsrKPE/l5uVzZRKFfGL+xSKcPribB2TH3TjKM4X6DxuP3kPH1sop/X9h5+zOt+ratno59m1eDW1TG+i6dUJhfi4uXzyLc0f34+VZn6J9d/n3TgghaoKKqipAtxpg7e6CSZ9VvaJJPFhLz844tGsT8nNGcwVcaQW5ORwP9h5U9UWIhOJRNW19mDbvVuEx6vIwcG6LEwfKzs2jeGfKrM+w8OO34Pvvd1yJqKZnwtuEE/0vwsjEjJNGEWHB8Pe+Aof+z5dJEhZv0TX3HIgbxzdyi3BllXrVQdVrm//8BQd3rOevScPEGnnBwZy4s3F0w91gP1h3GQmz1kXVgoQqIimR5bPxB1i06lUmSViMYmptK1eEVKFak2LHgaOfVfhY/xETceOTtxB+ejusOg/jQ+biJSghB1dzTGdqboW/F3/N44kMjU3Rpd9TPEKmfGKRKgnpkF7H2hXJITe5hVbH3KEknqaFJFQxRx/n2bXiQfmD5n/TiKKrF89yYpA6najC1LRFD5g078rvo6GLJ0IP/8Mz1unAvli/ERM5xju081/43SzqInJv3gZ9h33MyeX6hJKnbbtUfxxXkI83Fn3+DgrQBAbO7aCppYvYiAD8+eMXXCwx9f0vuHKxsWl8X7EQotGj4cNDe47Gmh0roWVmAxOPbiUtH+lRIQg/uRkeLq2rtfU4KyMdGWkpMDNTnPyigISCs7ioiAqPPTVhMqwdnHFw+wb4n9rC7UEOrh4YP+sztO/RnwOJkAAfaFu5VFgcUUzXzgOhV7xQEygoW/LlbMTFU3Xk2yULQ+jEO/LiAfhfOwrTFt1LkoSlGTq14URhZafj+XnZ/KvKf6iyJROnvo3Y6IiiOTXm9tAwtEB2YjTSokM4oLl17QLOn/CCrqUzB0U3r67ipSwvz5pXIbhKTohHRtxdPmEvRgk9arMwdGrFv5/aSirbxPygZKFSE6WiU+A2ihO4TZRVUVBQcTGJqqoaeg0ZzbcnEVVaUpLQqssIbgUq2RLYYQjuHN2AP36cDyf3ltU6DRZCCCEaml6DR+Pwrk28NM6+zzNQubfcjttCj6zjMSvVjQXioiM4oUYHkopQ3BTme75CXEPjcD763+98KEwtxDlZmdDVN8LAkZPQf8QkTiDRASyh8SiKUMUizYm+E+RbI4lCqvKiJKF1lxGc2KKvia47LdwfgXt/49eLYtLyKDlIMxcfVK1ZmJcDZVXFy12qijopaHbi+pU/IjnwKnRs3FGQl82t2mpq6rC2d8KSLz+ApqEZ1PTNEODvy1WMHXoOxOQZH5UZAUBJOuK3dRH/LEBoZiQlbenrL1509yjbxakbSE1bD67D31D4OI0zIoqWk1CHTlW7dBqa3NwcLPt6LlQNLOA05JX7yfp2/Xle+LUDqzhJOnDUM2hsJFEoxCOiNo7Le71wZstOJEREQEtfH+2HDETnsSOgrV92QLCofwZ2H4Ho+Eh4ndyK2GtHoWlqh9y0RKTH3IG9jQumP/dBtZ6PTgzpm31OarzCx2kGIS34KH1KV1rrjj34RtvpSPmTKxUVVRTkJtdoa0pl/Lwv406gD1yGvV5mqzAFKzTXJebqYYWLNoiujSsnXRN8L8CyQ8XhzlTFSa+TW4v/NmOP5hW+/dmPuH7hFLdpUIWjtb0N2j39Ajb9uRiFajrweHoaDwgnedmZuHtiE37/YR5Mf7DmYJjQNmaqbqRWDHPPAVwZSQnH6CuHEHJgFZJd2yE3LUnhyW9VUCVjRMhNhYnC/JwspEX4w7Hj02hsqN2Y/gyZty578ktBvU2PsRycnTywHSOefbXOrlEIIRqKUO9bOLluMwIuX6HSMLi19+TxNDbNql/tLR4vWn725offYPnXH+Lmmi+gY+3G72FauB+UlZXxxpyvYGphXa3npEUleUFBlR5wUqyqqa2r8DFTSxue6/bM6+/zpmQ6uCyt+KCXqh0VHV4X5Nw7EH7AttyqooQYzT00cusAs1LxAl03dZNoGJpzF0hlCVE6oKfFIxbtBlSYAUmzBlPv+qPlkLf/83XSnD63lu1wYt82nv+ooqGKPs+8gvA7Qbh48ggcB02GvkNLvm5qjU70v4yLRzdwleboF4oSd5npaTzCSFlDiw9NqQOHkpwJfhcRdfkQVxiaNOvMfzYeZes0/Z7j+7byckFFB/0UdxmZWtbaIpr66srpo7yMp9mQVytU9Oo7tOBKS+rYokR5Q56F+igkUSjEI8jJysZvM95HwIUr0Pd0gu6AZsiOTsbeFb/j5MatmL5yMQ+QFvUXfbN+YdTr6NG+P46e24eouEhom9uiy6Bn0a55Zw7OqoOSX55d++L6tXMwadG9QvCUFHwd2amJfIL4wOeppLS9hWdn3Fz5E28mU9Mtu9WvsCCft+K18qyZIeK0sERd1xDals4cOCQFe6MgP5cDLiM3T07mZKcoTohmJdPWtCaIvnKQq/wMnFvfC4wKkRJ6k5eedO0zpEY24lJLPg0qplvpBBSdyHo881aZlhiatUMn9T4xd3huz+SZH/M10fBjHUsnOA97rWRjMy2y0bZ0RNC+P5EYcJVeYEx89vNHukZqKV7+9RxeXkIn4aXfs7snNnOlQPcBI9DYBPp4w6jl/Tah0ujvDv0AQB8jhBDiwU5u2IIt3/wIDUsjGHR340qkGyfP4OLufZjwyWx0GiUbP+u7Zq074MtfN3LLsO+NyxyfuHV/Cd0GjHikeIlm6J09spfHseiVGs9SfHCa6HsRXXs/OB6l2K18kpDQvGaeE+17AeZtKx6CxvtegKq6Blw8iubZ/RcRd4KQHB8Dl27jkJUYjXif83zorqKhzfEoxZnUGq0oIUqtv5SUo7j5ztH1sOk+piQRRM8RcuAvPrzv1FvxxuLqsrJ1xMSp75T8P22FnjNlFM/mNnC8v0CExtoYubVHVmIMjuzZzO3fNGPv2L6tiI+Nhvu4d6FhYFby8ZQ01DCyQojXX4i8uJ+Xr5iYV/9nTM9u/bDpzyW8ZM9x8Mtlfk6hJGRiwGWMeeHNRpcMo3noWkYWZV7z0vQdWyLY7wJSkuLr3eif2iaJQiEewZ5fViDoujfcv30Weq3uV1xlv9gL/nPW4a858/DO6l/rxcIB8WCONi5wtJleI881eNzzuHLuOIL3rIRl5+FcMUUnrrRpLOLMTrRs3+2Rh/5SILNrw58I8VoFhwEvQk23qFIunyrlTm5BdmoC+o2YUCNfR35eLrc3+G5ZyFvsNIwsoKKujagQb070UVsuLVoxbdmjJNCgDcGhh9dy2zENjqYTT2qjUTtnDC1Ta2QnxfC8mObtOmPCK/cDKULboANuX+eqSyf35ny6/l9aWmnuZPm5OYROnPVd2vG2PEKVhNF3Q7lysjhJWPKxTZRg6TmQk5skJzPzka6ndcfuPN+FBm0n+V/m7YV0QpwccAXZaYl4+Z15jbK9lgJRSpZWhv48KSnXzIxXIYR4UoXd9OEkofmoDrCd2g9NlIriTpuXeiN06QH8O/9b2Lf0gIWzY11fqngIHT0DDBrzHN9qIvHo4tEGIV5/w6rrSBg6t+G4Lj0qGBFntkO5SSH6j5z0SM9Nc6K79h2KU4f3QN3ApGylXMBV7jrpN3wCNLXub1R+VHl5RV028bfOciKLEoQaxlbIiA7lOFTL3J4r5GhJXOnlH1SFR9t86ZpobmKC3yUkBl7jUTC82Tk8gJOEM+YtLHOd1C3m630Z6SnJMDa35DnZj/qznO/1ixzLGLt3UPi4kXt7PlSn5RsUG1OS2MCpjcKEFW0qVjcw4+UoebmVt1I/iJq6Ol6f8yUWz38Pt9d9BQOXdlyNSZ0tNBO9Tede6Dv8/rzsxoIqTel9qqz6tvBep5eSUuNLmzW+r1iI/yg7IwNnt+7ioKx0kpCom+rB5rV+8J+3EaE3bsGhVfM6u07x+FnZOWHmZz/i94Wf83wRFXUtDkhovh1tEXt+2pxHDjgokJk5byF+/vxd3qKra+WMJipqSI8IpO9imDJrHm+hJRlpqQj2v8WJNzvnptU+jbZ2cEHWni28Adht9Axomzvw/XnZGZzwpC3LaKKEoD2/8hw/2spLJ5RUfWjf9xkYurTlxynwiDy/G0lB1/naRs+cC/dWniWvQVZmOtatWIgLx734NSpO0LXu1APPvTn7ge0PtDyGNjIf37cNkWEh3PrdvntfZGVm8IDmylBikxKhJCkhtmQRi8LX3KTofqqkPLZvG7oNGF6t17Ho62nCW+HcW7TD4d2bEHLzOFeNtm7XmQPpR1kC8iRo3rYjbt68AgvPARWW8+RmpvLil2Z9Kt9CKIQQAjixYTM0zA1g+0rfkiQhaaKsBLvXByD5jD9O/bsVY+fOqtPrFI//MG7aR9/iz0ULcP3IOh69Qks2qJrQ1MoWU+f//EhVacUmvDITSYnxuLF/VcnsveykaN6OTJVrtH22OFajZSG0zdfIzAI2Di7V+jzm1nbcPUJJQprRRx079HVQAjAp8DpCDq8tOpj2+ps3PFP8mRYVgtAj62Do6sm/h7pEqKMn8sIeJAVeg4aGBs+5pgP40knCk147sH3NSm5Dvf/57fH0q7PQtHX7B15n1N1QHNmzCd6XznG7tHPT5jC51y6uVK6dtZiyatH9ubnFMWkczBw8K40lNWnmZBMlBPt6Iyo8tGTrcnXQnMGPf1yFI7s24tLpI0jJyoKVnSPGzPwEHXsOaJTbfSmpTp1GGTGhJT/vlJYYcBlW9s7Q1a9YgPCkk0ShENUUFRiMnIxMGHZX/AM+tSIra6gh5OoNSRQ2Qi7NWuHL5f/y7Lvw0EAeZNyyQ7f/FJAVs3Vyw/zlGzixdvPyWZ5n6ND1OU5gUTl8TnY2tvy1BCcP7kLevRkx1B7SoccATHzlbd6cWxWUdKN2W6tOQ6GkqnGvukuFE5+2vcYjIzYM6oW5UMrPgt/Wn7mVg+bt0UZmaqcopm/XlG+hh9chKS4Eri3alCQJaavb4i/eQ2iQHyw7D4OhazsOgJKCruHmxf1Y+MlbmP3tiqJrKYeCMAp+Lxw/AB0LB2jbtUJuZgrPsWmCQqCJCidoaaZiealht0uSc7QdjWQlxUDn3vDw0qjNhVAiNDL4Kh4Vfc2tOnbnmyhCVZYXTx1G2InNsOk6suS9olahUK+/eQYlVSwIIYSoXNC1G9Dv4sqJwfKUVJWh39kFgVev1cm1ibpFMR/NPowOvwPvS2e4Os/e2Z1n6f3X9lJVNXV+bj/vK7x9mDpDDFwd0bXf0JIqvIsnD2LL38uREBNZ8vtsnd0xaeo7cG7asmqfR1WN4099pzY8koRiTSVNHY4XDV3aIDcjBeGnt/EW3ltH13NClCrD6IAr4jlaAABKBElEQVTXvs+kkoNIGqfj0PdZpLp1QMCu5ZxkK50kPLZ3K9at+AGGbu3hPuAlqOsXbXuOvuSFn794F+98sajSZR5Xzh7Dbz98yt00+o6tOe6+efMGMk8e4sepM8XAqWIbdnLoTb4+OyfXkkrNrKSiuLM8+pqyEqN4tiD9Sgfkj5IoJGaWNtwiXbpNujFr3rYTzG3seXEQLTMpnt9YWFCAmOvHuAhi9MyPG2WXoCQKhaim4mG4hfkFlf5jzuXLpU52ReNCJ3ItPLvwrSYlxEYhPDSIB02/POszbiMonTxb/s1c3L5+GXp2TXn+Sm5qEp+0njvuhbAgf8z+7tcyv6c8+nNLm+62r17BLSqhh//h+5U1tGHSrAss2g/kgM24WRfcPbkZi9Z7wf/mNZz02oWr547B2L2jwuc19ugM/20XcCfQl5eCkCtnj3K7hevI6TwjsJiJR1c+0fPdtBBnDu9F76fGVHg+mkN48YQXHPq/wIFisbzOwxGwcyky4yMRfmYnbLqPLlOtRpWQqRGBePq5Bfz/dC104kuLS7QtHMp8LL0W0VcOQ0VLF0pq6goTluLR0WtPFbZrln6LlKDrRVsC83ORSlsC1TUw/ZPvG91AbSGEqC4lavmsJB4l9Fj5qm3RuFBVHt1qEh1MB/nc4I2xwyZNqTBCheYjrlq0gGdd69q6Iyshils4I+7excKPpuPdr5ZwQvFhs+N+/e5jbrVN9L/EN4pp9e2b80E2LTKhtt7w09vh2bU3VwlePXccW/9ayrGkoj/3Otau0NA3wZVzx0qqBLOzMrH172UwbtYZdr3uj/ChMTY6Fk7w3/ELNq9agjnfr6zwfIlxMfjth3nQtW/BHTUUI5PCTsN4VE/UZS9EntsNbXNHqGrfX2ZIc76jL3uhZYeuJeN2uvZ9Cns2rebFK+VnkaeG+fAoIBOPbkgOuSExaQ3/zDb9o+/w46dv49b6b3imp4qmLtIjA7kSlUYB1NQcy4ZGEoVCVJOlixM09fWQcPQWdNwrVoklnfFHQXYuXDr8t62u4slECaiM9FRQ4ZuWjuKNc4oCkbXLf8CNS6d5SDmhbXUDRk7C4HEv8Mkwzd27deUcNE1tkRx8g09eKZiihGFSwFXevLZ/6xoMnzSl0s+zbc0K7N+8mv/byL0DjFw9OWGYFHwDMdePciWh85BXoKKhxR9TUFDIydDoiDBcv3iq0vYKmilTHFgWO31oL3QtncokCYtRK7C+Q3OcPrS7QqKQ2lgO7doIQ5d2ZZKExZ/Hvu+z8Nn4A+JunkZGVDDPYFFSUeUT3ZS7fugxaBTadinanEev29jJ07Dim48QvP9PWHgO5M9NlYSUJKR2A9ue4xF96QA69+iLIF9vbnWmZC0FaW0790SXvk9xYosSpTcunOYWEltHF67yLK5YFIp16z+MT+jpNaXXVlldBX2fncoVCXSyLoQQ4sHcOrbHxUMHeT4hVRCWlp+VyzFp91Ej6+z6RP1GcRmNbNHW0eVKuIehQ+k9G//CoZ0beEsvoYQcJbyefu09GBqbIjcnG//+sRhaZnZIjwzipFfprpHs5HT8/r/P8OWvmyr9PKEBPvjhw2ncOUPVgbTxmCq9MuPCOR6lbhbXUdM5WUgFHJSwNLeyRff+wzlRSAfcilDMTbFibvb9OX/Xz5/kUThObftX/HhlZZi17oPgA6sUtvueOLCdgklOMBYnCYs/j0X7QUgKuoq89GT4/Psdt0LT9WbGRyAx4BIMjUzwzOvvldmcfPLgTvht+5lbpikOLsjL5XmLkef3clyfmRAJLR09mFvZYc+/q3Dj0lneTO3k5oGeg0fD2t4Jd4L8cObwHiTFx3Ic2qn3ID6cbYwVcVVFBRjzFq/G+eNeuHz6MLIyM9GsU3f0HDyyZKxTYySJQiGqSVVdHd0njMbBP1ZDx8OGW5CL//HNCI5B2PKDcO7QFtbu1ZvDIZ78BCGdsHptX4+I0MCS2Se0gIS23ipqA6Gk2KWTh7B6ybcoVFblpBUNa87PzuDquB3rfuN2j6dfe5eHINNpZVZCJJyHvsYDm4tZdRwK/x1LcWDLWgyd8JLCz5UQG439m9fwf9v2mgCTZp1LHqNknr69BwJ2LkOC/yWkRQTA0NSCt7QRa3tnDmZoSLaixB8NmaZAzsLmfoBFs1jUDStfWkILVJKCr1S4n7aOURuLU7shCn8fJfo0DczQrJkH8vPzcfPyAZ5/aO/igfHvfo723fuVCZbadu6Fl2d9ilWLvuStb8WoktCq0zAkB11DYV42B8ffzX4NGnrG0LZyQUpGOjatWoK9m1ZDVV0NibHR0Daz5WTp1fMnsWvDH3jhrQ/RqdegSr9GUdQCM+6lmlkmJIQQjU33iWNwdstOhPy0Gw4zhkBJXZXvz8/KQfD/dqMwOw/dJoyu68sU9czdkABONFEFHsU36ppaXNFGG3grO+SMjbyLZd98iAjqbGnVE/ZNO/LomZQ7PvC57IXv576Bud+vhN/Nq8hITQbSUmDi0YW3DRd3Y1l2HIKIs7sQc+0oLp8+gnZd+yj8XJv/WoqCQkDHygXOT00tScLRkkCaQ+i7dRGPLqEDXlqMVjz7kNqt9QxNOO40dK7Y7kszkNNj78LKfkTJfRRHK1PniJ7ir1vDyLLo4xLiKyQKaRGfro17yTbl0ijWpEPtRO8T6DVkNE4f2oPYmyehb2SCwaOfQZ+h48t0TtB/f/D1Mvzw0XSEHCw6tGdKSjB0bgs1fRNEX9zPB97z334BOTm50LP3gJKKJs4cP4hje7dwQpAqMdV09HkjdPbN63x/26598PI7nyrcYi2KUAFAj4Ej+CaKSKJQiEcwcOpkRIeE4vpXW6HtbAEtd0vkRCYh+WoIzJ3s8fxX8+r6EkU9s/nPX3Bwx3qu8rPv92zJyeraZd8j1N8Hz02bXSaBRTP8Vv7wKa6ePcZzApuNnwk1nXtVVjoGsOk2mmeoUABAAUhCbCQPqaZ2i9JJQqKqpQu7XuPhv/0X+N64xIN7yzt/bD+3y6vpGsO4acUWYmrBoNPMiAt7kZ+ZhlHPvVZyvW4t2vJpXOTZXXAa9lrJJmSSk5qImKtH0KZTT+gbGpfcb2BkgrCoqEpfL2pTMTA0qXB/ExR9ThpkXRl6zMDEDBOmzLz3/4o3mRXr2HMgJxIXzZvJ7d3K6pq83Szi3C5o6eqj37AJ2Lf5b1h1GQGzVj1L2lmykuPgs/F7qEIPTce/V7IUhZa+hJ/ajlU/LYCxmSXPrRRCCCFqGm0zfvbLT/DPxwuQciEYep2cufMg+WwACnPz8eI3n8PY5r/PSBZPjoBb17Dos3egrKUHy45DoaZH8/hCcfLwXj7onP3tcp57XRrNxv7jpy94bpttj3Ewad615DFKBlLc6bvpfxzn6ukb8UI7FU1tWHcbVZIkJBQ/0SEsbUc+sX+7wkQhHVz73bjE/23V8akylXqEYjRahBZ66B+En9oKSzsnON+Ls+ggvNfgUdj97yoeh0NjZYrRtdNCPqqc7NLnqZL7KTbNz8nmdmDakFweHcDzxxkZK4xJ6XkrQ49Ra+voF97g28PiUWpDXrBsA/748Que8UhdPbSZOCXEmw/k+wwbhzOH90HVwAKuAyeXdOwU5ufDb/tiXmRIRQUUxzdRUi7ZRH3t6Hps+mMxFxYIUVUytEKIR6CsqoIXv/0CUxd/DycHd6gGpcFM3RiTPpuDd9b8Bj2Tit9MROPlf/MqB08UMDkNmcItvXQi6jhwMux6T8Kpgzt50HRpO9f/jqvnTvAGXxOPzveThKXQrEA1LT0+pVTX0EJhXi4MnBQnpbQtHHnQcuDtGwofT0yIRRNlVf64yuYZ6Vg6Iy8jFa7NW/PG3mIUmL38zjzkJMfAd+MPiLpyiJOg4Wd3wW/zQmhramDCK2+Xea6u/Z5CamQQ0mhrcznUlkHVfdSCWp6eoTHMrOyQFFCx2pCkx9zhBF7TVveXqlSl3cLAyBgebTtxAJmfnckt2/TfbTv1xLWLp7ii0rx17zKvTWZsGL/mToNeKrM5mZa+2PWeCE0jc3htW/fQzy2EEEI8qraD+mHOljXoNmoENCNzoRWdj57jx2Lu1n/Qok+Pur48UY9Q9eAfP86HurE13Me+yy29Bo4teOaf25hZSM/MxMY/Fpf5PeEhgfjzp/mcUFTV0oNxs04VnldN1wiGru15ZjWPDmkCPhgvn+QjlDik0TERd0MVXmNyYty9D2zC7cuK0OxAkpeWgCnvfFomzhsw6hk4ulEXzFLcOboBiYFXEet9Cv5bf0Ki/2W8MG0OtHXvzwts3akHNDS1eV51xdcrDzHXjsDB1UPh8hD3Vp5cvZiXlV7hMUrSUVdK01ae1YpHi+ec6xuZ8lxHikfzc7N5EYyysgrPVLTv/3xJkpCvsyCPF/PR+0mJW0oSFn0+JRi5toO550Cc9NrJ26eFqCqpKBTiEdE/9s26d+abEA9ybN9WaBqaw7RFxYCdZgHG3zrFH9OyfdEJbU52FlcKmrbohtgbJ8okoUqjAEzd0BxJ8TFo3bEHD5amFgzFCnkjcGWb7ugEmJJeVAFYGQpWKPjrOWgUVFSL2puKUVBGLSd7N/2NS6f2c0UkBV49+j/FcxTLt7JQy6+LR2sE7fsdFp6DOHFK7RWUYKTWCit7J3TpO0Th37v+IydyJaaO9WlOlhYHXnR9YUfXw9TSFi3aVf3vZV5uLm9gDg64DbN2A7ldhUZBJgVexdlj9LXkckJX0cY6LVNbhe8PBcIGru1x/dxunD2yD57d+vCWQCGEEKKmmdhaY8SsaXV9GaKeu3X1PHeguI2eCaVybag0S9C0VR9cOb0TKUmJ0DMwLFkgR50pGgZmXNVWnIQqT9PEGrHeJ+DRrhMnqB5YaZefV2kbrC5VJPIHFSIvIwWq2vqK41EefeMCG0fXsl+HujpmfvYTDm5fh2P7tiHE5xwnHT3adMLgmXO4C6bsx2twtR9tPaavz6x1L6hzleUdXjhCcxHHzlik8Fq7DxiOfZtX8/I/WrBX3IJMFX7hZ3cgMzEa/UZ8juoo3sBMm5Jde0zi9yU9OgQxlw/i7q5NHHdSwrY0Gv1TkJNV+VLBpp0QeX4P1i7/HuNfnglDE7NqXZNonCRRKIR4bGjmXmJEFJ9oGllZcmVmY0DLL2iunaKTRLqPtrCFh94quS8s2J+HRNu6tUe8z3lkJcUqfF4KwnJS4qCr35qXftDMwgT/y9wmXF5KmC+3Jts5N8X+Lf/wXBoaYm3n5MoDkGnY8c51v/H8QRqWTMOjS8vNTEMiVfEVFFZ6Imlp68AzUF6YPpdPPDW1tPlkVBGq1pv+yQ9Y9+v/cP7YToSf2V7yGLW8jHnhzUq3uvUYOBJ3gwNwfN8mxN04wVv1aO5MSugtTkhO+2hhpZ9XkYsnDyHg1tUKG5iptYUqLOlUOiP2Lm/XK40CQWW1yjfPUXsMnSivWjQfW1cvx1uf/lAyR0cIIYQQdSctMQnpScnQMzGCpq4uGoOIsGCoqGlUWqlHI2bCC/IRExlWkii8de0C9Jxa82FySpgPxzWKOk+yk2OgoaUDbR09uHq0QoDPNdh0H11mHE1xlR4dCnfu0Y87Z2jGdnxsFPT0DdG592B4du8LB/cWCPG7yYlHalUujdp3Y71P8kzoQtoMqAAlC5+aMJkPqrMy0qGiqsb3VYZG+NABOM1G9KXtyveoqKmj95AxcGqqeEMzxZxvzP0aS7+ag5trvoAeVVGqqCL1zm3kZKRg0qvvwrlpS1QVLVXZ/NcS3sBMLcTFPzeo6bThGeW+m3/kFmlFidfiuFOR4vuvnjuJK2eP8yK//iMqHoALUVrj+CldCFGn6Jv6mU3bcWTNesTfCef7tI0M0W38KPSf8nyF6rQnDSW8MjOLtsMpQkk4OtEsRonU4opBalOO9znLs/FKtxkQStxlpyaic5/B/DmeGvcCJ/so2WXk1r4kwKAtvuEnNvEcF5p7QoEIBTPKmsa4cvEcb0cbOvEleHbtg0tnjiFw969Fi1PsmvIEFjrJvHtiM89KQW42D2J+EHo/q/KeqqmpIzszkxOeFLRqGlvzRmXaULx4/rtcFZidnQUVFVW06tANnfsM4eQjfV00Z4UWkxzfvw0RYSHQ09DEwJemo0ufIbxNujpOHdoFPRs3hYtYdK1dOFlIQa1t97ID4amSMOrKQU7AqigIzlLu3IaGsSUc+7/Ap82L5r2Dz5b8w0G0EEIIIR6/sFu+2LvsN/icOsdVa9zq2bcHnpo2FWb2ihNoTwp1dU3ukijIzVa4gCPvXqxaJiYtKIAKtQu7tUfcrdNICrzOrcPl49gE3wvo3ncIx2jPT5uDedOe4TmCNJe7OFlYkJuDO8f/RX5WBi9FObF/G3TM7aFhbI3wuFg+WD2081+MeHYqlsx/D9FXDkNJRQ0mLXpwnJWbnsJxF3V8cLuz8YMr46iLpqoxobqGBjLSUriCT9vcAao6hshOisXhXf/Cz/syj77JzcmBraMLLxShw3FCc7+/WLKONyB7Xz6H/Mw8NO/RF72GjOEtxNVx+fRR7iqiJS3liwvo/TJr0wdhx/7lSkXqVCqmYUSdLU047qTqwfJS7hQVIzgPewPJITd4XiG9dp7d+lbr+kTjIolCIUSt2/b9zzixbhOMennAdUpXKKmqIPGMLw7+8TfueN/ClB+/eaKrC6nNdvs/v3KAQ5uJS6O5JjTDZNDI+yd71Eahqq6BpKDrMGvTl5NU/juW8KkqDYzOz8lC/O2ziLq4H+269oW9SzP+fbStLi46AmcOr0PM1UPQNndEbnoSUu768bKR1OQENNHSg8eYWdxGQihJF331MHZv+BOvvPcFfL2vICMrC0F7f+OZhtRikpeZCnV9U942l5cQzrNTasJJrx24du44nAa/DH2H+6e11FJx99RW3LpxhU9QC3JS4PPbIuz59y/M/Gwhvz4UQFH7SPkWkkeREBsDDYuKVZjFKIlJsw+pWpNmvRSjtpDIi/sQfnob7HpNLDOwm2YsJod4w7b7WGgYmsNx0Mu4tfZLnD28F/1GTPzP1yyEEEKI6gm6fA3L35wFNUsDOMwYDA1rI2QERcNv2yX4v/A63vpjCS+IeVLRiJv1vy5EvO95mLXsWeHx+NtnYGRqCRt755L7qDrwxvWrsOz4FPQdWyL0yFrkpCVwQqp463HE2Z1QU1HGgHuxrKmFNV6f/SV+/f4T3Pz7Mz6cpvZfqrQryMtBszYdcPv6Jd5orGdXFMOSjNgwBO1ZiRP7tqHn4FE4vm8bIi8eQNTlg1DR1EFuRgovmzPx6Ia4W6fQpe/9pST/BXXKrFr0JY/Bse/zdEl7NR20U1vv3ZBA6OYpcVwccngfDu/aiDGTp2HgqGf446iVd8QzU/n2XyTGRUNNSxdqOgYKH9cyteFf6fDeeeirJTMgKbmpbmCGyPN7uSq09Fxz6riJOLeHk586lo684CUrMQp7Nv3Ny2SqMjdRNE5P7k/mQoh6IfTGTU4S2r0+AOYj7y+Y0GvrAIPObvD5aD0u7dmPjiMrLq54UnTrPwwHt69H0N6VsOvzdMlMOwpAwo5ugJqqGnoOuV+tRlVztOzj5MHd0LNxh8uIaRyYUfKuGH1j7zFoJMbf2+xbfHL6wlsfolPvwZyEi464Cy1DXYwcMZurOtcs/RbNJr1RkiTk51FSgkW7/kgL9+fAh5KFi+a9DS0Ta2gYW0FFUxdq2vq8eIQSmvT8lc2Vqa4je7ZA36FlmSRharg/JwlpnqNV52HcwlE8jyZk/5/4+Yt3sWD5v2VOu/8rXX0DJCbfG56tQE5SDHT19BF6aA3ib56EjrUbn7rTibaqiioS/S4iKy6cT9spaE6+c5uThPR1FQ/9pqCPKjSpzUYShUIIIcTjRZVx6z//Bpqu5nD7chKU1Ip+DNZtaQfjvi3g894abP7mR0xb+TOeVEam5ujcdwjOHdvNo1OMXNqhibIyH0DToTF1qjw3bXaZ8S19ho7jrceUhLLv+yyPiqH/jji7q+RjqGPl1ffn89beYq06dscXS9fh+P7t8Ll+iePQ9kNGoWOvQfhuzmswa92nTJKQ0Pw9y07DcP3oenz84yr43byGuNhoHtGjqqUPFS1dFOblIfb6Ebi3ao9W92Z7/1enD+3mPx823UaXJAmpRTpwz0re3uw2ZmbJRmS6P+riPmxZtYQXnFDHS03R0TNEbmY6x5iUGC2PKhxJRnQwbq9dAH2n1rz0MDX0FrKTorn123fj/zgepZ81spKikeBznrdQ06F88c8PRu6dEOL1F5LiY2VeoaiUJAqFELXqzOYd0LAwhNmw+5VYxfTbOUK/vRNObd7+RCcKdfT08fbnP+GXBe/DZ+MP0DKm+X9NkBEfwa0M0z/7kefylTb6+TcQFhQAv+2LoW/XDHp2Hpy0S7vrB01tHcz49H9wcC0bYBUHALRhrfSWNbJq0QJom9lBw6Ds5ylm4NwGQcc3wrlZS7w1739Y/+uPiPG9UPI4bV976e1PeZZhTaBlJ5F3gmDX6/72ZBJz7RgPxKYN0aVPOel01L7/C7i1/mtcOHGQk681hWbirF+5EJkJUdA0uh/klmxgDvPBM6+/x5v8aMh0RNBlnnXTb+gYnmuTEBuNA9vW4vqZHfx7NIwsYNN9DEyadS4z9JtOonNyKm7GE0IIIUTtVxPG3bmLpt8/V5IkLKaiqwnLSV0R+N0OxIaGwdTeFk+qZ157FzlZmbh0ZB2izu2Gmq4Bt7LSIo8Rz76K7gNGlPl4J/cWGPfSW9j052KkhnpDz6EljJt2RlLQVeRlZfAswBHPvKLwc1HicNRzr5W5z+faReRmZxUtsVOA2prvHF2P0AAfvLtgMf5Z9j2unTvBsxGLZ1zTmJkJr7xdrXnUD3InyI+7ZkqP+EkOvoGc1AQ0nfB+SZKQUBWfZcehSI8Kgde2dTWaKKTFd//+vggxN07AquOQCnOxY68fg3OzVtzaTUtmrl88zfG0q2sz9JkxG1b2zrzE5cjuzYjNySpKBru356Rs6SpFGvNDcnNzauzaxZPnsSQKlyxZgu+//x5RUVFo3bo1Fi9ejI4dFW/lEUI8WWJCw6Dd3BpNlBVv26WT3NjN9wcHP6msHZy5Eu7K2WM864ROVmnrL5X9K6rQ09DUwjvzF/HG3JNeOxEfcJETjv2encrLPLR1qzfnjj5f6dbY8koSWoWF8GjTEZ8vWYtg35tIiIviE07X5q2hfK/FoSbQtdAtPye7zDWm3PWBdadhClsh1PVNoGPhiFtXztVoopC2Kx/duwVBu5fzSTa1FNOW6MTAa4g8twtWdk6cTKQqRmojVxQI03v546czcTc6lrcJlkfbqKlq06NrxVYfIcTjIfGoEI0XJQCp/VWneVH7pqJ4lD/uzt0nOlGoqqaOqe/Px5BxL+D8cS+kpybD2NyKk2+VVZf1HzkJju7NcWTXRvjdusYxWvsuPdF32HjYObtX6/MX3Ev4VRqT8qKUJigoLOQD2tfnfIWE2CgE+9/mzhmXZq34/ppEcTjNbSy/BJBmZ5df7seX2KQJDF094X98I3Jzsvk1rQk6egYYPPY5HgdUmJ8L05Y9oKptwBuYoy7s5cPrUTNnw8LGnmd10628MS++yT9z/PnjF3AdPROahhXfU1pKQ0UHhsaKiweEeCyJwg0bNmDWrFlYvnw5OnXqhJ9++gmDBg2Cr68vzMyk1FWIJ52GthYS4qIrfTwnLgUaOkUnW086OgWlBRx0q2rg0mPgCL79V1QpeO7YfuSkJvIsk/KordjWya0k2KEgiLa8OUHxprf/ioK9Fp5dEeh/gQOhooCxkLcqN7nXbqwILVQpXvZSU2gRzKz5i3mI9q3DaxF6eG3JY3SNL874sEqtzn2eGovl38xFvO+FChuSab5OTloSb5gWQjx+Eo8K0bipa2nxYWhuQhrUjHUVxqNEQ6fs4rgnFc17pltV0fbe6mzwrYydkzuUaWxL0HVYtK0YD1MlH8WDFLeWPpAt3dZc01q078qL/TLi7kLLpCiRTBWMxeNvFCl+jFqWa9KwSVP49dm3aTVirh3l5Db9uaXZkdM//g6uzcsuklGkXZfe2PDbIkSe2Q6HQS+VzDIkGXHhSLh1Br0Hj6qxBKd4MlVeXlJDFi5ciKlTp+Kll16Ch4cHB2haWlr4448/avtTCyHqgTYD+yLlWggyQ4vmapSWl5qJhCO30HaAbN2qbR17DuTTQ2rnoFk0pav44m6d4bl6/YaXbQOubQNHPY3M+CjegEebg5s0UYKWmS2Sg70VfjwtfkmPDIKDW/MavxY9A0PMmLcQny1Zy/N5qK3j86XrMf2T76t8ct26Uw906z8cd46sQ9C+3zlhSK9twI4lvHiGWnrsnNxq/NqFEA8n8agQjVuz7p2hqqGOmJ2Ku1hidl2GnpkJ7Ft6PPZra0yoO4a6NGKvHkJ6dGiZx7KTY7mTg+YPWtk+vqUybTr2gKmVLUK9/uaqPaJ9b5Fdbnqywt9DsaqFrWONzswuPqh/avyL+G7Vdrzy3uc8+mbGZz9iwfIN8GhbcaOxIpQAnDJrHtIiAuC38QdEXz2CBP9LuHN8IwK2LYalrT2GPV00s1CIOqkozMnJwaVLlzB37twyVST9+/fHmTNnavNTCyHqiTYD++HQn2sQMG8T7GYOhl4bB/4mmB4QhTuL90NVSRXdJ42t68t84lEr85tzv8Hi+e/j1toFMHBqA2UNLaTd9UV6TBhXutESlMeJTkWpWu/vX75BcuBVaFs4Ij8rnVss4n3Ow7jp/ZZAGh4ddnwTz6OhRS+1hQZT0+1R0J9rSjLSKfihXRs5YUhonsyzL3yNNp2l7bgyNCfn6pljuHT6CLKzMvg96D5wJKztner60sQTQOJRIYSmrg56PzcJXr//DWUtdZ6dTb/mJmcgauNZxB/yxriP3uPuD1G7xk95C+F3guC37Weew02zqSlJmBR0AybmVnjp7Y8f6/XQez5z3kIs+mwWzxKnJKESjwUqROjRDXCiqrxS1YWJAVeRFHKDk3i1tTVYQ1Mb7bv3f+Tf37xtJ8z+7lfs37wGV87u5Thaz9AEQ8Y9z63k9HOBUOxOoC8vhYyJDIe2ji53gtFynpocwdQQ1OpXGxcXh/z8fJibm5e5n/7fx8enwsdnZ2fzrVhKSlEJuBCi4VLTUMcby3/C77M+hN+H66FmpAslFWVkxSTBwMocry7/EYYWZf+NELWXmPt00d84tncLrpw7juycHDg6uKDXqzPQwrNLrQU7D9K5zxA0bd0ep7x24W6wP5QdbJGSlAi/o+uR6HseunbNeI5hUsBl5GWmYOr7C2p8Nk1Notewa7+hfMvJzkYTpSY1tiX6SZUYH8ubtqPuhvAMStpqGOh3gAd1D534EoY/rXhIuhBVJfGoEIIMeuNl5GZn49jf/yJy3WmOSbNjk9AETTBs5uvoOm5kXV9io0BJMFpUcv7YAZw8uAvxwVegq2eIfpPfRLd+w7gD5nGjBOW8n1fj6rljuHb+JPJycqDm6oyLJw/j9vqvYODiyUtAUsN8kRrujw49B1ZY/FLfUBfL1Pe/QH5+HvJyc7n6sS5i/YaCuqz+/e0njj/VdQ2haWqH3MgAXDp1GHbOTTFj3v94jmRjUa/Sol9//TU+//zzur4MIUQNMzA3w6w1K3njnN/Zi/wNy6FlczTr0UVObh8zE3NLjJ08jW/1BW18poRQ6W/Uxw9sx5Gd/yLm8kGoqqvBs1NP9B0+ATYOLmgoaDOyeDB6r5d+NRuJSUlwH/cutEys+X46+Y65eoQHepta2KBzn8db7SoaN4lHhXgyUSXxiFnT0PPZ8biy/xDSEhI5Rm07uD90DBtPAqA+oPbYbgOG862+UFFV5Sq+0pV8/Uc8ja2rlyHQ5xzPyLZ1csX4d+ahfY/+/OepIaBKuMZWDfcoKEFIN5tuo2HSvGvJose0qGCEHFiF336Yh7e/WITGolb/xJiYmEBZWRnR0WUXGdD/W1hUHEhKLSE0aLr0Ca6t7ZO7dUqIxoROsJw92/BNiMpkpqfhn+Xf8+ld4b0B0Xl5OUhNSYaegXFdX56oYX7eVxAW6AvnYa+XJAkJDd628BzAbegHtq5Fp96D5BRcPDKJR4UQpVFysM8LT9f1ZYh67vi+rdj+z0reDF0sJSkB2nr6DSZJKKqGksAUbxq5tecli6VRt4t1t9Hw8fqbu5+qswSoIavVP+Fqamrw9PTEoUOHSu6jzUD0/126dKnw8erq6tDT0ytzE0II0ThQW8TPX7yLq+dPwbrrKLScPB+tXv4Kdr0mwPfmdSz8eDqyMjPq+jJFDbp15RzUdQyga6046DJ0a4+IO4FITox77NcmnhwSjwohhKiOI7s2Yu3yH6Bu7Y6mEz5A66nfwXXkW8hR0caS+e/D57ripTiiYYoKD0VSfAyM3DsofNzAoSVU1DRw88o5NBa1ngqnE9mVK1fir7/+wu3bt/HGG28gPT2dt84JIYQQxa6cOYpgX284Dn4Fpi26Q0VDG8pqGjBu2gnOw99AdEQYzhzaXdeXKWpQXl4ulFTUKq0WVL433zEvN+8xX5l40kg8KoQQoiroUHrbP7/CxKMr7HtPgqaRBXc66Fg6wumpqdAyt8eWv5bW9WWKGpSXVxRnUkyqkJISmiircFFDY1HrzeoTJ05EbGwsPv30U0RFRaFNmzbYt29fhYHSQgjRGFFVy+1rFxAW5MezUVq27wZzq8bZ4nbmyF7oWjlzIFaehqE59B2a49ThPegzbHydXJ+oeQ4uzXBoxwZkJUbze1xecshN6BoYwdDEtE6uTzw5JB4VQogHi4m8ixsXTiE3Nwe2jq5o1roDlJSL5rQ1JtfPn0R2ZgZc2vat8BjNrTNt1RvB+/9AZFgILG0d6uQaRc2in73UNLQ47tQ2t6/weHp0CHIz0+Dg5oHG4rFMtZw+fTrfhBBC3Bfsdwu/L/wMcVHhUNXQQn5eLjb9sRhtOvfC5Jkf8Va4x5Go9L1xCRdPHERGWipMLKzRbcAwWFhX/CZZFUkJsTi+bxuunD3OW39tHJzRa/AoNGvT8aEz5pIT46FuUPkP7ZRISgm68kjXJeqnNl16QVffCHdPbILjkFegrHp/AQxtFUzwPY+hE16UIdyiRkg8KoQQiivo/lr8Fa6cPgIlFVUoq6giNysDRmaWeOXdz+Dk3uKxXEdcdCROHdyJqLuhUNfQRLsuvdHCs8sjJSvz8/Jw8dQhnPLahbiYKOjq66Nzr0Ho0u+ph8bXKckJHI+o6RopfLz4YDMlKV4ShU8I+vPWvf9QHDuwgwsTtEslC/OyMxBxehtMLGw4ed5YSOQthBB1gNpof5o3E6p6ZnAbPQNaZvYozM9DYsAVeJ/ZhqVfzcU7Xyyq1QUOmRnpvHHW3/sKNA3NoapjAO9rl+C1bS0Gj3sBI599tVqfnxKfP38+Czm5OdB3bAUVQ234B/jh2uez0PupsZg49Z0HPp+hsSlCwiMrv974SBgYS2XZk0RVVQ2vzV7Asyl91n8NA9f2UNXSQ3pkIJJDvOHeqj0Gjnmuri9TCCGEeCIVFhZixbcfwe/Wddj1nghDl7ZooqzKy8QizuzAT/PewYc//AYLm0c7QK6qg9vXY/OqJVBWU4emqS3yM9Nw9she2Dq7461P/gc9A8MqPxcdVP8y/z34eV/mGciaVu5ITo7Dv7//jCN7tmDWgp9hYFR5PGlgZIL83GxkJ8dBXd+kwuNZCRH3Pk5i0ifJiGdfRZDfLfhv/wUGTi2hZe6AnNREJPlfhIqSEl6d/3OjWmIjiUIhhKgDB7b8AyirwWnoqzyHjzRRUYVx045Q0dSB397fOMBxb+lZo9WDV88ew4kDOzhRSRWEuXl5cH5qKnRtm3ISryA/DzHXjmLfpr9hZGqOnoNGVem5c3OysfTL2VDWNYbH4Ck8X7A4AI2/fQZH92yCnbM7uvYbWulzdOk3FN7ffcyVZOWXW2TEhSM59CaGvvbuf3wVRH3j4tEaHy38k1uQadt1dlYmzK3t8NRr76Jrv2Hcki+EEEKImhdw6xpuXz0Pp8EvQ9/hfuUgVVRRjOr773fYv2UNXpzxUY1+3og7QTi6ZzNuX7uErMx0pCYlwKx1H1i0H1jSXZAWGYTQg39j2ddz8cE3y6p8eL3176UI9PWGy/A3oWvtUnJ/VlIMAnctxx8Lv8CsBYsr/f2tOnSHprYOoi57wa73pDKfl+Pkq0fg6N6CYxXx5NDQ1MKs+Ytx4sB2nNi/HdHn90BDSwc9+g9Fv+ETYWxmgcZEEoVCCPGYUfLswsmDMGreoyRJWJqeXTNoGprh/HGvaiUKqXUkKT4Wmlra0DcyqdCCsfKHTzlRSDMA1YxskRVzHvZ9n+HPV4yGNVu064+shCjs27wG3QeMqNLpGSV4UpMT0GzIayVJQkLBFQ2DTrlzG4d2/vvARGGbTj3g1rIdAvf9AbN2/WHk0g5NlJSQGHQNMZe8YOvohs59hlT59RANazbMM6+/xzchhBBCPB4XThyEhr4J9OybV3iMEnZGTTvhwslDeH763CpXU9HBdHxMJMe7lFwpPz7k/LEDWLVoAR+M6zm2RGbQNehYu8Kq87AySTkdSyfY9pqIwD0rEXj7Oh8sVqVb5uTBXTxHsHSSkGgYmMGqywj4ef2N8JBAWDs4K3wONXUNjJ08DWuWfIuCvFyYterFlYUZsWGIvuyFrIRIjH1nbpVeC9GwqKmro9/wCXxr7CRRKIQQj1lBfj5ysjIrnX1CQZKqjiEyUlOq9HzJCXHYvnYlJxbzcrL5PqemLTFs0svwaNOR/3/f5r9x7fzJkhPj6Ks0h0YNBs5tFD4nVTYG7FqOqLshsLJzeug1+N+8Cm1TW2gYKG7DMHBqjdDDazmAo0SmIhRITvvoO2z8fRHOHNmHyHNFG46VlJTh2a0vJr02i4M3IYQQQgjx32WkpXDMWVm1HsWqFFvm5eY8NAajBCFVCR7cvgEJsUWjZPQMjXn8zKDRz0JZRQXR4Xc4SWjg0g52vSbw/Lc475Mw6TpK4TXo2rpDXUcfNy6erlKikJYD5mZnVRrfGji05IUk/reuVpooJHRQrqKihq1rVsBv66KS+63snfHa5z/BpVmrh16LEA2ZJAqFEOIxo0BJz9AE6TGhnJArj9oaMuPCYeLZrkpJwm9mv4a09HSYtu7Lp6856UmIuXkaiz9/F1PenYc2nXrxTBbjZp1L2kpoHiINrKYKQoXXqKbJv+bm5FTti+LgrrDSh+lUueTDHjJM+LlpczDq+dcR5OONgsJCOLo2q1Ah+bjQnJvTh3bj6N4tSIiNgoqqGpzcmmPClBkwa6TbqYUQQgjxZDA2t0TWxbMoyMvhA+Ty0qNDoaNvCFW1+8vGKovz1i77jqv5jFw94dSBqgOVkBR8HTvX/Y5Q/9t4bfaXOLZvK5TVNWHbcxyaKCtzPEqUFHTYEHoO6r6hETdV8bD25MKSWPXhbcyd+wxGh579EXj7BtJSkmBsZsljdGpzfviD+N64zLMc/W9dQ2FhAUzMLDFo7HPo2HNgnVyPeLI1nmmMQghRj/QYOAJJ/peQlRhd4bHY68eQm5mGbv2HP/R5tq1ZgfT0DLiNfhsWngOgY+XMAZrLiGkwcG6NNUu/Q3hoINKSE7mqr5imiTXystKRHnNH4fMm37nFQWFVk2FuzdsiPfYuz39RJCnwKmwcXau8yVlHzwCtOnbnduS6ShJmpqfh+7lvYP3KhchQ0oJxy15QN3fGzSvnMG/6M9i94Y86uS4hhBBCiJpAs4Bzs9IRc/14hccopkv0v8gx68OSYzRX+6TXTtj1HM9jbfTtmkHP1p3/33HgZFw7fwKXzxyBn/cV6Np78GE1oQVm1IKceue2wuelhSIZCdGwdXKr0tdj6+QKNXVNJAVeUfh4cvANFBbkw62F4opDRd0ubi3aol3XPrB3KZrnXRf2/LsKP37yFvwC/KDftAv03TohOjYWfyz8HPPffpE7doSoSZIoFEKIOtB/xESYWdogYPsviLp8kJd1pIYHIOTwWkSc241BY5576IY5Cgpotoxxi+5Q0y27DY5m+1l2HMpzCymxVeR+xZ+ebVNuJwk/tQ35OVllnzchEnE3TvBJamVtwuW169qb20vuHF7LSc7SJ8wxN47zjMLEuBicO7YfDcW/vy9CxN1QuI95G85DpsDCcyAc+j+HZpPmQEVTFzvX/4GLJw/W9WUKIYQQQjzyjOAh419E5Pk9CDm0hhfKUUwadeUQx6gmZhYYMPLphz4PLX/QNLLgmYbl6Ts0h66VM47v217UWnKvy4RQVSF1vMT5nEN6VEiZ30fzAe+e2gotHT14dutXpa+HDqR7DBrBi/lSwnzLPJaZEIW7J7dy6/GR3ZuRk102/q2vfK5fwo61K2HRfhCaTZwDq45DYN1lOFo8+wkM3drzvMWlX82u68sUTxhpPRZCiDpA29Te+3IJNv+1hIc6U4BGDE3MMenVWeg1ZMxDnyMxPoZnxuhYKp6xoq5nBA1dI2RlpHMSLzHgSsk2YUokOvR/HgG7V+D2+m84SKPEYXp0CJL8L8Pc2hajn3+jyl8PVR9O//h7/PTZO7j1z3xucVbR0EFKmA+yk2N5Vgyd4P754xfczkun0/VZanIizh07AIsOQ6BlWraqUl3PGNZdRiDk4GpsXb2Cg9e6OmEWQgghhPgvRjwzFYbGpti7eQ0Cdi7j+2jUSoce/TF28nRo6eg+9Dmiwu9Ay8Kx0nhI29IZUcGX0bHHABzbv6NMqzMt0aMNx/47lnD3i66NG3LTk5HodwF5GSl488NveMlEVY167jVE3AnG7d0reHuzlpk9x6IUk1IMZ9S0I04f3sPzEmd+9iOPBKrPjuzaCA1DCz6wLv36UpLVrsc4JAd7w9/7Cu4E+nJrtBA1oX7/rRBCiCeYtq4eXpg+F+MmT0d0RBhU1dRgZesIJWXlKv1+DQ0t/pWCKUUoCKP2Ygrw+g4dxwtPKFFISTsKNCh4onaQkP1/IvrKQRQWFEDf2BRPjX8efYdPrHI1YTEKTj5b/A++nf0qEkNvQVXbgFucbXuNh66VC1cX3jm2AZv+XIyOPQfwPML66k6QH8+KNHBSPKxa37Elz7eJj45AdMQdWFg/uPpTCCGEEKI+opiw5+DRvMAjIiyY51NTpWFVEoTFNLV0kJqmOB4lND9bU1MbvQaPwpHdG3Hn6HrY9Z7EyUK6OQ2egsBdK3hUTWLAZU5UtuvSGwNHP8Oja6qDD68/+R4bf/8ZR/dsQW5GKlR1DGDbYxyP51FSVeO2aL+dy3D5zFFOiNZnNJPQoGkXhUlY+lr07ZshKfgGrpw9JolCUWMkUSiEEHWMAjFHN49q/z4jU3M4uHog/tYpGDi34oHPpSX4XUJeThbadukNU3Mr3A0NxMWDq6F97Qi0zB2Rm56E5NBbMDQxwztfLIKRifl/PlXNy83m5BnNpzFya1/mMQpwLNoNxC3fC7h69jg69R6E+kpJqei1pCpIRYruL2qdyc7MfKzXJoQQQghR0+ig2sbB5ZF+r2e3vtiw8keu3FPXNy3zWG5mKpIDr2HwmGdhammDKbM+w+8LP+MOFD37FtyOnBp6C7lZaXh++hx06jWIr+W/dGvQbMG7IYHQs3GD87DXKjxOB+fUDn3q4K56nyik+L6yeJTQY9ROnZ2Z8VivSzzZZEahEEI0YMMmvYzUyCDcObwOOWmJJTNd4m6dQfjpbejYaxCfClPARYHZtI+/h7OjA5SSwmCoVojxL7+FT376C6YW1jXSepEUH8e/ahpbVdoOraquhcS4iktc6hNKwNJpdqL/JYWPUxs3odfVxELx1yqEEEII0Rh07jMIhqbmCNqzkuccUhcJ3WhrctDuX6GhqYmeg0fxx9JikHmL/0HPAcOgnhkHtfQYdOnVH58s+htd+w3leLQmRrokxMVAo5J4lNBjCfU8HiXN2rTnw3/q/CkvLzsDyaG3UZCbDQtbhzq5PvFkkopCIYRowFp4dsFLb3+Ctct+wM2Ay9DUN+FlInnZmejQcwCen3Z/uDEFXS3bd+VbbdHRN+Bf6URZUbIwNyMFudkZ0NUvu3ylvqG26x4DR+Lo3i3QMrWDnr1HSdBKcxwjzu7kjX3tu/WFto5eXV+uEEIIIUSdoSUis+b/jKVfzeE5h2ra+jwPOzs1ESYW1njjk8XQNzQu+Xha6Df+5Rm1ek16+gZITI6t9HGKVc30jVDfDRg5CRdPHkLY8Y2w6T6mZGM0xfohXn9TSSFU1TV4rI8QNUUShUII0cBRi0brjt05iIiJuAsNLW14du0Dc2u7x34tFPhRNV7M9WPQt2/Bg5ZLi7l2jCv12nTphfpuzOQ3Eex3E0H7fudTZ21TW2QlxSA9Kpi/Ln1DE4x98c26vkwhhBBCiDpnYm7FXSq+Ny7D90ZRBZxzs1Zo3rZTledv16TOfYZg/cqFvO2YNjKXRpudaSvyyDfr/7Zge5dmmDj1HWz49UckBV2DnkMLFObnITnkJgoL84GCQrz41lxO1gpRUyRRKIQQTwAKDmgIdX0w+oU3sGje2wja9xss2g+GlpkdclITEXv9GGK9T2DEs682iCo8VVU1fPDtChzeuQEHtq5FvN8FoKAAyiqq6NL3KYx45hXoGdT/k2ghhBBCiMeBui+atvLkW13r0ncIju3dgqDdy2HZaVjJgrrEgKuIPLcL1vbO6NBzIBqCPk+NhaNrM+4guht4FQU0s7CwEE5NW2Hks6/AvWXdv97iySKJQiGEEDXKvWU7TPv4O6xZ9j38ti7i7cC0+ENdUxujX3iTN9g1FLTUpP/Ip/mWmZGOrIx06Ojp80a9+i4jLRVnj+7jk30KJl2bt+EEJ23bFkIIIYR4kqlraOKd+Yvx188LcPPwWoQeXlvyWIv23TB5xodQU6//8Vwx6tj5cOEfyMvNRWpyItQ0NBrEwXtBQQFuXz2PCye8kJaSAhNzS3QfMLza26zF4yWJQiGEEDWuebvO+HL5v/C5cQlx0REcyNA8RQraGiqaW0i3hsD/5lWeE5SVmQEdSyfeKHjj0jLsXPc7Xp/7FZq17lDXlyiEEEIIUav0DAzx1qf/Q3T4HQTcusbxEB2c0qichkpFVRWGJmZoCDLT07Dky9kIuHUVWsaWUNExgp/vTRzdsxm9h47DxFferpHFNaLmSaJQCCFEraB5NB5tOtb1ZTQ6SQmx+GXB+1AzskbzMc9CVbvotDk3MxV3jqzD0i9nY97iNTxLSAghhBDiSUdzu+tidndjt+rnLxES6Avnoa9B18aNk4KF+fmIu3UaR3dvgomZJfqPnFTXlykUUFJ0pxBCiMYtLMgP548dwNVzJ7gqTTQcJ/ZvR15+PhwGTi5JEhJVTV04DHgRhUrKPLNHCCGEEKI+oxbbS6eP4MKJg4iJvFvXlyOqIToiDNfOHYdVl5HQs3UvqRykhYCmLXvAuGknHNi2Dvn5eXV9qUIBqSgUQghR4m5IAFb/8g1CA26X3KeuqYUBI5/GUxMm88w+Ub9du3AKevYtoKJesc1bWVUd+o6tcP3CaYydPL1Ork8IIYQQ4kFysrPx7+8/4czhPcjPu59I8mjbGS+8NQcGRqZ1en3i4bwvnYGSsgoMXdoqfNzIvSP8fc4hPCQQds7uj/36xINJolAIIQSj+S3/+2g6mmjowmnwy9CxdkVeRiq3B+za8Acy0lMxYcrMur5M8RB5uXlQ1tSo9HFlNQ3k5uY+1msSQgghhKjq8osV336I29cvw8JzMIzc2kNJRRVJId4IvLAXP3w4DXO//02Ws9Vz+Xm5nChsoqw45aR8bzEgLWcR9Y+UhgghhGC7N/yJAmVVuIyYBn2HFlx9pq5vAusuI2DVaRgO79rIi0lE/ebo2hRpYT4oLCio8FhhYSFS79yGg0vTOrk2IYQQQogHuX3tAm5ePguHAc/DvG1fHqOirK4JY/cOcB4xDQlxsTJCpQGwc26KvJwspEcGKXw8OfQWVNTUYWFj/9ivTTycJAqFEEIgJzsLl04fhrFHVw7GyjNt3g0qaho4d3RfnVyfqLpeQ8YgKyUeUZcOcGKwtJirR5CZGI3eT42ps+sTQgghhKgMtRtrGVtBz86jwmPqesYwcGmDU4f21Mm1iapzb9kO5tb2iDizHXnZZeedZyZEIvb6UXTsOQBaOrp1do2ictJ6LIQQAulpqTwDRsPIUuHjSqpqHJwlJ8Y/9msT1ePg2gyjnnsN29asQOpdXxg4tQGaNEFy8DWkRQbjqfEvwq2F4nkxQgghhBB1iWJNNUPzkuUX5WkaWiAm+Ppjvy5RPfT+vfLuZ1j4yQz4bPgWhm7t+WeJ9Og7SAq8AgtrO4ydPK2uL1NUQhKFQgghoK2jC2UVVWTFR0LfrlmFx/Nzs5GdEg99Q5M6uT5RPYPHvQAbR1cc2vkv/C7sBhUWujRrhX4vvYbWHXvU9eUJIYQQQihkYGSCu97e3BWhKFmYmRAl8WgDYevkho8W/olDOzfg3LEDiElLgZGpBYZPfAl9ho2DhqZ2XV+iqIQkCoUQQkBNXQPtu/fF1UunYOzRGSrqWmUej/M+yXNGOvcZUmfXKKqnhWcXvhW3H1d2Mi+EEEIIUV906fsULhz3QnLITRg4tijzWHZyLFejUaJJNAwm5paY+MrbfKss+SvqH5lRKIQQgg2d+DKUCvIQsH0JkoKuIS87E1lJMbh7aisizu1G/xGTYGxmUdeXKaqJAjIJyoQQQgjREDRt1R4t23dD6KHViLrkhZy0RORlpiH+9lkE7FwKY1MLnscsGh6JRxsOqSgUQgjBzCxt8P7XS7F6ybcIPvBXyf0aWjoY8eyrGDz2+Tq9PiGEEEII8WRTUlLCqx/Mx8Y/FuP0od2IvLC36IEmTdDSsyuemzZbFmAIUcskUSiEEKKElZ0TZn+7AuGhQYgMC4a6hiZvLaPWZCGEEEIIIWqbqpo6nnn9PYx89lX437yK/IJ82Ds35TZWIUTtk0ShEEKICqztnfgmhBBCCCFEXdDW1UObzj3r+jKEaHQkUSiEEKJRycvNxe1rF5CanMib9dxbeUJZWb4dCiGEEEKIx+duSADCgvygoqqGZq3bQ0fPoK4vSQgmPxkJIYRoNM4e2YvNq5YiNTmh5D59YzNMmDIDnl371Om1CSGEEEKIJ190RBhWLfoSwb43Su5TVlFFt/7DMH7KDKiqqtXp9QkhiUIhhBCNJkm4atECGLq0Q9OBL0PD0ByZ8RGIuuyFld9/AqXZX6Jt5151fZlCCCGEEOIJlRgfix/mvok8JRU4DpwMPbtmyM/JQrzveZz02omUpAS8NvtL2RAs6pRS3X56IYQQovbl5+VxJSElCe37PQtNYys0UVKGlqktB2n6ds348YKCgrq+VCGEEEII8YQ6uH0dsnKy4Tx8GgycWkFJRRWqWrqwaNsPdn2extWzxxDsd7OuL1M0crWSKAwJCcGUKVPg6OgITU1NODs7Y968ecjJyamNTyeEEEI8UNFMwgSYt+1b4YS2SRMlmLXpi7iouxKYCfGEkZhUCCFEfXLm8F4YunXk5GB5Bs6toaFvzF0wQjxxrcc+Pj5clbFixQq4uLjA29sbU6dORXp6On744Yfa+JRCCCFEpaiNg1C7sSIaBmZlPk4I8WSQmFQIIUR9kZ+fh4y0FJhUEo/S4bWanimSEyUeFU9gonDw4MF8K+bk5ARfX18sW7ZMgjIhhBCPHW03JjSTkNqNy6P7iz7O9LFfmxCi9khMKoQQor5QVlaBjr4hMuPDFT5eWJCP7MQoGLZs/tivTYg6mVGYnJwMIyOjB35MdnY2UlJSytyEEEKI/8q9lSdvN6bFJYWFBRWCsugrh2BuYw8H12Z1do1CiPoRk0o8KoQQorZ06zcUiX4XkZOaWOGxBN8LyE5LQtd+Q+vk2oR4rInCgIAALF68GK+99toDP+7rr7+Gvr5+yc3WtmLVhxBCCPEoJ7gTp8xAcshNBO39HWkRgcjLTEPqXT8E7v4VaZGBmDhlpmyYE+IJV5WYVOJRIYQQtaXfiEnQ1dNHwI5fEH/7LHIzUpGVFIPwszsRdmITuvR9CnbO7nV9maKRq1aicM6cOfxD1INuNAumtPDwcG75GD9+PM+EeZC5c+fyKW/xLSws7NG+KiGEEKKcdl374PXZX0EtJwX+O5bgxl+fImDXcmg1ycFbn/wAj7ad6voShRD1ICaVeFQIIURt0TMwxAdfL4OLW1PcOb4R3n/Pw+313yDZ9xwGjXkOz0+bU9eXKET1ZhS+++67mDx58gM/hma/FIuIiECfPn3QtWtX/Prrrw99fnV1db4JIYQQtaFN555o1bE7QvxuITkpnmcSUruxVBIK0bDUZkwq8agQQojaZGhixofUcdGRuBsSAFVVVTg3awUNTa26vjQhqp8oNDU15VtV0KktBWSenp74888/oaT02MYhCiGEEJWi70dOTVvU9WUIIf4DiUmFEEI0dCbmlnwTolFsPaaArHfv3rC3t+eNcrGxsSWPWVhY1ManFEIIIYQQogyJSYUQQggh6kGi0MvLi4dF083GxqbMY4WFhbXxKYUQQgghhChDYlIhhBBCiOqpld4LmhlDwZeimxBCCCGEEI+DxKRCCCGEENUjQ1qEEEIIIYQQQgghhBCSKBRCCCGEEEIIIYQQQkiiUAghhBBCCCGEEEIIIYlCIYQQQgghhBBCCCEEkUShEEIIIYQQQgghhBBCEoVCCCGEEEIIIYQQQghJFAohhBBCCCGEEEIIISRRKIQQQgghhBBCCCGEIJIoFEIIIYQQQgghhBBCSKJQCCGEEEIIIYQQQgghiUIhhBBCCCGEEEIIIQQAFdRjhYWF/GtWenpdX4oQoo7kZmQAmcpQzSqAUk4mMrMy6uxasunzZxb9e5STlYHMDPm3SQjxcFn3/q0ojmtEwyLxqBCipmPbTLV05GRKLCmEqJ/xaJPCehy13r17F7a2tnV9GUIIIYQQ/1lYWBhsbGzq+jJENUk8KoQQQojGFI/W60RhQUEBIiIioKuriyZNmqAxSUlJ4aCU3kQ9Pb26vhzxAPJeNRzyXjUc8l41HPJePRyFWqmpqbCysoKSkkx9aWgaczxK5O94wyHvVcMh71XDIe9VwyDvU83Go/W69ZguvrGfvNMfcvmD3jDIe9VwyHvVcMh71XDIe/Vg+vr6dX0J4hFJPFpE/o43HPJeNRzyXjUc8l41DPI+1Uw8KsfaQgghhBBCCCGEEEIISRQKIYQQQgghhBBCCCEkUVhvqaurY968efyrqN/kvWo45L1qOOS9ajjkvRLiySZ/xxsOea8aDnmvGg55rxoGeZ9qVr1eZiKEEEIIIYQQQgghhHg8pKJQCCGEEEIIIYQQQgghiUIhhBBCCCGEEEIIIYQkCoUQQgghhBBCCCGEEJIoFEIIIYQQQgghhBBCEEkUNjDZ2dlo06YNmjRpgqtXr9b15YhyQkJCMGXKFDg6OkJTUxPOzs68fSknJ6euL00AWLJkCRwcHKChoYFOnTrh/PnzdX1Jopyvv/4aHTp0gK6uLszMzDBq1Cj4+vrW9WWJh/jmm2/4+9Lbb79d15cihHgMJB6t3yQerd8kHq3/JB5tuCQmrRmSKGxgPvjgA1hZWdX1ZYhK+Pj4oKCgACtWrMDNmzfx448/Yvny5fjwww/r+tIavQ0bNmDWrFkcKF++fBmtW7fGoEGDEBMTU9eXJko5duwYpk2bhrNnz8LLywu5ubkYOHAg0tPT6/rSRCUuXLjA/+a1atWqri9FCPGYSDxav0k8Wn9JPNowSDzaMElMWnOaFBYWFtbg84latHfvXv7GsnnzZjRv3hxXrlzh01xRv33//fdYtmwZgoKC6vpSGjU6saWTwV9++YX/nwJoW1tbvPXWW5gzZ05dX56oRGxsLJ/kUsDWs2fPur4cUU5aWhratWuHpUuXYsGCBfw96aeffqrryxJC1CKJRxsmiUfrB4lHGyaJR+s/iUlrllQUNhDR0dGYOnUqVq9eDS0trbq+HFENycnJMDIyquvLaNSo1ebSpUvo379/yX1KSkr8/2fOnKnTaxMP//tD5O9Q/USn7UOHDi3zd0sI8eSSeLThkni07kk82nBJPFr/SUxas1Rq+PlELaCiz8mTJ+P1119H+/btee6IaBgCAgKwePFi/PDDD3V9KY1aXFwc8vPzYW5uXuZ++n9qzxH1E52y03yRbt26oUWLFnV9OaKc9evXc9sUtXkIIZ58Eo82XBKP1g8SjzZMEo/WfxKT1jypKKxDVF5OgzYfdKNvGvSNPTU1FXPnzq3rS260qvpelRYeHo7Bgwdj/PjxfPouhKj+yaC3tzd/8xf1S1hYGGbOnIl//vmHh7ELIRouiUcbDolHhXj8JB6t3yQmrR0yo7COZx3Ex8c/8GOcnJwwYcIE7Ny5k7/5F6PTKGVlZTz77LP466+/HsPVNm5Vfa/U1NT4vyMiItC7d2907twZq1at4rYCUbetHtQitWnTJt5aVuzFF19EUlIStm/fXqfXJyqaPn06vy/Hjx/nrY2iftm2bRtGjx7N34dKf1+i71P07x1tRC39mBCi/pJ4tOGQeLRhk3i04ZF4tP6TmLR2SKKwAbhz5w5SUlJK/p++6dN2LPomQwNxbWxs6vT6BCqc3Pbp0weenp5Ys2aN/MNUT9DflY4dO3JFRHEbgZ2dHQcAMjy6/qBvSTTQe+vWrTh69ChcXV3r+pKEAlRVFBoaWua+l156CU2bNsXs2bOlNUeIJ5DEow2LxKP1k8SjDYPEow2HxKS1Q2YUNgD0zaM0HR0d/tXZ2VmCsnoYlNHJrb29Pc+BoZPfYhYWFnV6bY0dbWikE1uaq0QBGm3BSk9P528kon61d6xdu5ZPb3V1dREVFcX36+vrQ1NTs64vT9xD7035wEtbWxvGxsYSkAnxhJJ4tOGQeLT+kni0YZB4tOGQmLR2SKJQiBrk5eXFA6PpVj5oluLdujVx4kQOlD/99FP+Zt+mTRvs27evwkBpUbeWLVvGv9IPOKX9+eefPERfCCGEEA8m8Wj9JfFowyDxqGjspPVYCCGEEEIIIYQQQgghW4+FEEIIIYQQQgghhBCSKBRCCCGEEEIIIYQQQkiiUAghhBBCCCGEEEIIQSRRKIQQQgghhBBCCCGEkEShEEIIIYQQQgghhBBCEoVCCCGEEEIIIYQQQghJFAohhBBCCCGEEEIIIYgkCoUQQgghhBBCCCGEEJIoFEIIIYQQQgghhBBCSKJQCCGEEEIIIYQQQgghiUIhhBBCCCGEEEIIIQSRRKEQQgghhBBCCCGEEBD/Bz+8Z7N57ElJAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(16, 4))\n",
    "tree1 = DecisionTreeClassifier().fit(X[::2], y[::2])\n",
    "tree2 = DecisionTreeClassifier().fit(X[1::2], y[1::2])\n",
    "\n",
    "disp = DecisionBoundaryDisplay.from_estimator(tree1, X, response_method=\"predict\", alpha=0.5, ax=axs[0])\n",
    "scatter=axs[0].scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\")\n",
    "axs[0].set_title(\"Decision Tree trained on one half of the data\")\n",
    "\n",
    "disp = DecisionBoundaryDisplay.from_estimator(tree2, X, response_method=\"predict\", alpha=0.5, ax=axs[1])\n",
    "scatter=axs[1].scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\")\n",
    "axs[1].set_title(\"Decision Tree trained on the other half of the data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear that in some places, the two trees produce consistent results (e.g., in the four corners), while in other places, the two trees give very different classifications (e.g., in the regions between any two classes). The key observation is that the inconsistencies tend to happen where the classification is less certain, and thus by using information from both of these trees, we might come up with a better result. We will cover multiple trees further on in this notebook. Here we see that with slightly different data set we obtain a different model, so we have a high variance with higher depth tree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning for trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import the familiar dataset `breast_cancer` from sklearn datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 30 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   mean radius              569 non-null    float64\n",
      " 1   mean texture             569 non-null    float64\n",
      " 2   mean perimeter           569 non-null    float64\n",
      " 3   mean area                569 non-null    float64\n",
      " 4   mean smoothness          569 non-null    float64\n",
      " 5   mean compactness         569 non-null    float64\n",
      " 6   mean concavity           569 non-null    float64\n",
      " 7   mean concave points      569 non-null    float64\n",
      " 8   mean symmetry            569 non-null    float64\n",
      " 9   mean fractal dimension   569 non-null    float64\n",
      " 10  radius error             569 non-null    float64\n",
      " 11  texture error            569 non-null    float64\n",
      " 12  perimeter error          569 non-null    float64\n",
      " 13  area error               569 non-null    float64\n",
      " 14  smoothness error         569 non-null    float64\n",
      " 15  compactness error        569 non-null    float64\n",
      " 16  concavity error          569 non-null    float64\n",
      " 17  concave points error     569 non-null    float64\n",
      " 18  symmetry error           569 non-null    float64\n",
      " 19  fractal dimension error  569 non-null    float64\n",
      " 20  worst radius             569 non-null    float64\n",
      " 21  worst texture            569 non-null    float64\n",
      " 22  worst perimeter          569 non-null    float64\n",
      " 23  worst area               569 non-null    float64\n",
      " 24  worst smoothness         569 non-null    float64\n",
      " 25  worst compactness        569 non-null    float64\n",
      " 26  worst concavity          569 non-null    float64\n",
      " 27  worst concave points     569 non-null    float64\n",
      " 28  worst symmetry           569 non-null    float64\n",
      " 29  worst fractal dimension  569 non-null    float64\n",
      "dtypes: float64(30)\n",
      "memory usage: 133.5 KB\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "X, y = load_breast_cancer(return_X_y=True, as_frame=True)\n",
    "X.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the features are numerical. Let's check descriptive statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean radius</th>\n",
       "      <td>569.0</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>28.11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean texture</th>\n",
       "      <td>569.0</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>39.28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean perimeter</th>\n",
       "      <td>569.0</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>188.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean area</th>\n",
       "      <td>569.0</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>2501.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean smoothness</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.16340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean compactness</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.34540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean concavity</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.42680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean concave points</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.20120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean symmetry</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.30400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>0.09744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.405172</td>\n",
       "      <td>0.277313</td>\n",
       "      <td>0.111500</td>\n",
       "      <td>0.232400</td>\n",
       "      <td>0.324200</td>\n",
       "      <td>0.478900</td>\n",
       "      <td>2.87300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>1.216853</td>\n",
       "      <td>0.551648</td>\n",
       "      <td>0.360200</td>\n",
       "      <td>0.833900</td>\n",
       "      <td>1.108000</td>\n",
       "      <td>1.474000</td>\n",
       "      <td>4.88500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>2.866059</td>\n",
       "      <td>2.021855</td>\n",
       "      <td>0.757000</td>\n",
       "      <td>1.606000</td>\n",
       "      <td>2.287000</td>\n",
       "      <td>3.357000</td>\n",
       "      <td>21.98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>40.337079</td>\n",
       "      <td>45.491006</td>\n",
       "      <td>6.802000</td>\n",
       "      <td>17.850000</td>\n",
       "      <td>24.530000</td>\n",
       "      <td>45.190000</td>\n",
       "      <td>542.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.007041</td>\n",
       "      <td>0.003003</td>\n",
       "      <td>0.001713</td>\n",
       "      <td>0.005169</td>\n",
       "      <td>0.006380</td>\n",
       "      <td>0.008146</td>\n",
       "      <td>0.03113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.025478</td>\n",
       "      <td>0.017908</td>\n",
       "      <td>0.002252</td>\n",
       "      <td>0.013080</td>\n",
       "      <td>0.020450</td>\n",
       "      <td>0.032450</td>\n",
       "      <td>0.13540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.031894</td>\n",
       "      <td>0.030186</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015090</td>\n",
       "      <td>0.025890</td>\n",
       "      <td>0.042050</td>\n",
       "      <td>0.39600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.011796</td>\n",
       "      <td>0.006170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007638</td>\n",
       "      <td>0.010930</td>\n",
       "      <td>0.014710</td>\n",
       "      <td>0.05279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.020542</td>\n",
       "      <td>0.008266</td>\n",
       "      <td>0.007882</td>\n",
       "      <td>0.015160</td>\n",
       "      <td>0.018730</td>\n",
       "      <td>0.023480</td>\n",
       "      <td>0.07895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal dimension error</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.003795</td>\n",
       "      <td>0.002646</td>\n",
       "      <td>0.000895</td>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.003187</td>\n",
       "      <td>0.004558</td>\n",
       "      <td>0.02984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst radius</th>\n",
       "      <td>569.0</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>36.04000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst texture</th>\n",
       "      <td>569.0</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>49.54000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst perimeter</th>\n",
       "      <td>569.0</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>251.20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst area</th>\n",
       "      <td>569.0</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>4254.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst smoothness</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.22260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst compactness</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>1.05800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst concavity</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>1.25200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst concave points</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.29100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst symmetry</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.66380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <td>569.0</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>0.20750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count        mean         std         min  \\\n",
       "mean radius              569.0   14.127292    3.524049    6.981000   \n",
       "mean texture             569.0   19.289649    4.301036    9.710000   \n",
       "mean perimeter           569.0   91.969033   24.298981   43.790000   \n",
       "mean area                569.0  654.889104  351.914129  143.500000   \n",
       "mean smoothness          569.0    0.096360    0.014064    0.052630   \n",
       "mean compactness         569.0    0.104341    0.052813    0.019380   \n",
       "mean concavity           569.0    0.088799    0.079720    0.000000   \n",
       "mean concave points      569.0    0.048919    0.038803    0.000000   \n",
       "mean symmetry            569.0    0.181162    0.027414    0.106000   \n",
       "mean fractal dimension   569.0    0.062798    0.007060    0.049960   \n",
       "radius error             569.0    0.405172    0.277313    0.111500   \n",
       "texture error            569.0    1.216853    0.551648    0.360200   \n",
       "perimeter error          569.0    2.866059    2.021855    0.757000   \n",
       "area error               569.0   40.337079   45.491006    6.802000   \n",
       "smoothness error         569.0    0.007041    0.003003    0.001713   \n",
       "compactness error        569.0    0.025478    0.017908    0.002252   \n",
       "concavity error          569.0    0.031894    0.030186    0.000000   \n",
       "concave points error     569.0    0.011796    0.006170    0.000000   \n",
       "symmetry error           569.0    0.020542    0.008266    0.007882   \n",
       "fractal dimension error  569.0    0.003795    0.002646    0.000895   \n",
       "worst radius             569.0   16.269190    4.833242    7.930000   \n",
       "worst texture            569.0   25.677223    6.146258   12.020000   \n",
       "worst perimeter          569.0  107.261213   33.602542   50.410000   \n",
       "worst area               569.0  880.583128  569.356993  185.200000   \n",
       "worst smoothness         569.0    0.132369    0.022832    0.071170   \n",
       "worst compactness        569.0    0.254265    0.157336    0.027290   \n",
       "worst concavity          569.0    0.272188    0.208624    0.000000   \n",
       "worst concave points     569.0    0.114606    0.065732    0.000000   \n",
       "worst symmetry           569.0    0.290076    0.061867    0.156500   \n",
       "worst fractal dimension  569.0    0.083946    0.018061    0.055040   \n",
       "\n",
       "                                25%         50%          75%         max  \n",
       "mean radius               11.700000   13.370000    15.780000    28.11000  \n",
       "mean texture              16.170000   18.840000    21.800000    39.28000  \n",
       "mean perimeter            75.170000   86.240000   104.100000   188.50000  \n",
       "mean area                420.300000  551.100000   782.700000  2501.00000  \n",
       "mean smoothness            0.086370    0.095870     0.105300     0.16340  \n",
       "mean compactness           0.064920    0.092630     0.130400     0.34540  \n",
       "mean concavity             0.029560    0.061540     0.130700     0.42680  \n",
       "mean concave points        0.020310    0.033500     0.074000     0.20120  \n",
       "mean symmetry              0.161900    0.179200     0.195700     0.30400  \n",
       "mean fractal dimension     0.057700    0.061540     0.066120     0.09744  \n",
       "radius error               0.232400    0.324200     0.478900     2.87300  \n",
       "texture error              0.833900    1.108000     1.474000     4.88500  \n",
       "perimeter error            1.606000    2.287000     3.357000    21.98000  \n",
       "area error                17.850000   24.530000    45.190000   542.20000  \n",
       "smoothness error           0.005169    0.006380     0.008146     0.03113  \n",
       "compactness error          0.013080    0.020450     0.032450     0.13540  \n",
       "concavity error            0.015090    0.025890     0.042050     0.39600  \n",
       "concave points error       0.007638    0.010930     0.014710     0.05279  \n",
       "symmetry error             0.015160    0.018730     0.023480     0.07895  \n",
       "fractal dimension error    0.002248    0.003187     0.004558     0.02984  \n",
       "worst radius              13.010000   14.970000    18.790000    36.04000  \n",
       "worst texture             21.080000   25.410000    29.720000    49.54000  \n",
       "worst perimeter           84.110000   97.660000   125.400000   251.20000  \n",
       "worst area               515.300000  686.500000  1084.000000  4254.00000  \n",
       "worst smoothness           0.116600    0.131300     0.146000     0.22260  \n",
       "worst compactness          0.147200    0.211900     0.339100     1.05800  \n",
       "worst concavity            0.114500    0.226700     0.382900     1.25200  \n",
       "worst concave points       0.064930    0.099930     0.161400     0.29100  \n",
       "worst symmetry             0.250400    0.282200     0.317900     0.66380  \n",
       "worst fractal dimension    0.071460    0.080040     0.092080     0.20750  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "1    357\n",
       "0    212\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check do we have any missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean radius                0\n",
       "mean texture               0\n",
       "mean perimeter             0\n",
       "mean area                  0\n",
       "mean smoothness            0\n",
       "mean compactness           0\n",
       "mean concavity             0\n",
       "mean concave points        0\n",
       "mean symmetry              0\n",
       "mean fractal dimension     0\n",
       "radius error               0\n",
       "texture error              0\n",
       "perimeter error            0\n",
       "area error                 0\n",
       "smoothness error           0\n",
       "compactness error          0\n",
       "concavity error            0\n",
       "concave points error       0\n",
       "symmetry error             0\n",
       "fractal dimension error    0\n",
       "worst radius               0\n",
       "worst texture              0\n",
       "worst perimeter            0\n",
       "worst area                 0\n",
       "worst smoothness           0\n",
       "worst compactness          0\n",
       "worst concavity            0\n",
       "worst concave points       0\n",
       "worst symmetry             0\n",
       "worst fractal dimension    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's split our data into test and train, leaving 20% of data for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(455, 30)\n",
      "(455,)\n",
      "(114, 30)\n",
      "(114,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's tune our tree using randomized search cross validation on the train set, and evaluate its performance on the test set.\n",
    "We will optimize several decision tree parameters:\n",
    "\n",
    "- *criterion* {\"gini\", \"entropy\"}, default=\"gini\"\n",
    "The function to measure the quality of a split. Supported criteria are \"gini\" for the Gini impurity and \"entropy\" for the information gain. In practice, both the Gini impurity and entropy typically yield very similar results.\n",
    "\n",
    "- *max_depth*  specifies the maximum depth of the tree. The default is None.\n",
    "\n",
    "- *min_samples_leaf*  int or float, default=1, specifies the minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least *min_samples_leaf training* samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we are not doing feature scaling, since decision trees do not require it, as this is not a distance based method. As the scoring function we will use f1 score. (Recall that the metric depends on the problem we are trying to solve, so here f1 is just as an example). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import randint\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "distributions= dict(max_depth=randint(1, 10), \n",
    "                    min_samples_leaf=randint(1, 10), \n",
    "                    criterion= [\"gini\", \"entropy\"] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we used the `randint()` method which returns an integer number selected element from the specified range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_iter=20,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                                        &#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019B78868320&gt;,\n",
       "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019B7891A630&gt;},\n",
       "                   random_state=1, scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomizedSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_iter=20,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                                        &#x27;max_depth&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019B78868320&gt;,\n",
       "                                        &#x27;min_samples_leaf&#x27;: &lt;scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019B7891A630&gt;},\n",
       "                   random_state=1, scoring=&#x27;f1&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: DecisionTreeClassifier</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=6, min_samples_leaf=3)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=6, min_samples_leaf=3)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=10, estimator=DecisionTreeClassifier(), n_iter=20,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019B78868320>,\n",
       "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_discrete_frozen object at 0x0000019B7891A630>},\n",
       "                   random_state=1, scoring='f1')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier()\n",
    "random_search = RandomizedSearchCV(tree, distributions, n_iter=20, random_state=1, cv=10, scoring='f1', n_jobs=-1)\n",
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we specified that we want to test 20 different parameter combinations, where we could either use gini or entropy as the splitting criteria, the tree could be of depth between 1 and 10, and the minimum number of samples required to be at a leaf node would be a random number from the range 1 to 10.\n",
    "Let's see what parameter combinations were generated. We will look at the *cv_results_* once again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame(random_search.cv_results_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "Now, let's see the value of the column with the tested parameters, *params*. We will sort the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'criterion': 'entropy', 'max_depth': 1, 'min_samples_leaf': 1},\n",
       " {'criterion': 'entropy', 'max_depth': 1, 'min_samples_leaf': 5},\n",
       " {'criterion': 'entropy', 'max_depth': 1, 'min_samples_leaf': 7},\n",
       " {'criterion': 'entropy', 'max_depth': 2, 'min_samples_leaf': 1},\n",
       " {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 5},\n",
       " {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 8},\n",
       " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 5},\n",
       " {'criterion': 'entropy', 'max_depth': 8, 'min_samples_leaf': 2},\n",
       " {'criterion': 'entropy', 'max_depth': 8, 'min_samples_leaf': 7},\n",
       " {'criterion': 'entropy', 'max_depth': 8, 'min_samples_leaf': 7},\n",
       " {'criterion': 'entropy', 'max_depth': 9, 'min_samples_leaf': 6},\n",
       " {'criterion': 'entropy', 'max_depth': 9, 'min_samples_leaf': 7},\n",
       " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 5},\n",
       " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 9},\n",
       " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 5},\n",
       " {'criterion': 'gini', 'max_depth': 4, 'min_samples_leaf': 3},\n",
       " {'criterion': 'gini', 'max_depth': 4, 'min_samples_leaf': 9},\n",
       " {'criterion': 'gini', 'max_depth': 6, 'min_samples_leaf': 2},\n",
       " {'criterion': 'gini', 'max_depth': 6, 'min_samples_leaf': 3},\n",
       " {'criterion': 'gini', 'max_depth': 8, 'min_samples_leaf': 4}]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(results.params, key=lambda x: (x['criterion'], x['max_depth'], x['min_samples_leaf']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what are the best parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini', 'max_depth': 6, 'min_samples_leaf': 3}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's see what is the best f1 score of cross validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9546061823172776"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot look at the evaluation metrics of our tuned tree on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.91      0.92        43\n",
      "           1       0.94      0.96      0.95        71\n",
      "\n",
      "    accuracy                           0.94       114\n",
      "   macro avg       0.94      0.93      0.93       114\n",
      "weighted avg       0.94      0.94      0.94       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "y_pred=random_search.best_estimator_.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at our ROC plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x19b784e20c0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1ld9++01eeuklrgjvwBhADoEgIvLQAJg/f37LdS8vLxk+fLiry2TsZZCYAENE5DkBMDo62uEXzJMnz8OUx7A4BpCIyAMDYN68eR/Yb4W17vCYpKQkV5XNUFgDJCLKXjgXaFZPhM0aIBGR5wTA+vXrZ35JDI6zwBARZS+cDDsLJCdrcvlmnLrOGiARUfbAAJgFrsbGSWKyJl45RAoG+bu7OERExACYNSKj7tb+QnL7i683dzkRUXbAo3EWCI+6rf5nBigRkYcHwMTERNm6dat89tlnlnlBL126JDExMa4unyFwIVwiIgPMBHP27Fl5/vnn1WK4cXFx0qRJEwkKCpL3339f3Z43b17mlNQA06BxGSQiIg+uAWLy6xo1asj169clZ86clu1t27aVbdu2ubp8hhBxrw+QNUAiIg+uAf78889q8musBWitePHicvHiRVeWzTAiou/1ATIAEhF5bg0wOTnZ7nRnFy5cUE2hlP5SSERE5KEBsGnTpjJz5kzLbcz/ieSXcePGSfPmzV1dPkOIjL7XBMoASETkuU2gH3/8sTRr1kwqVqyo1gXs2LGj/O9//5OQkBD5+uuvM6eUHuzmnQSJiUtU19kESkTkwQHwkUcekT///FOWL18u//3vf1Xtr2fPntKpU6cUSTGUcghEkL+P5PJ3encTEVEmcfqIjFpfQECAdO7cOXNKZNAMUA6CJyLy8D7A0NBQ6datm2zZskUlxJBjYwAZAImIPDwALlmyRG7duiWtW7eWokWLyjvvvCN79+7NnNIZQMS9adA4BpCIyMMDIAa8r1q1SiIjI2XKlCly9OhRefrpp6Vs2bIyYcKEzCmlEWqADIBERMaYDBtj/nr06CGbN29WyTC5cuWS8ePHu7Z0BsA+QCIigwVAJMOsXLlS2rRpI08++aRcu3ZNhgwZ4trSGSgLlDVAIiIPzwLdtGmTLFu2TL799lvx8fGRl19+WdUCn3322cwpoYcLvzcLDGuAREQeHgDRB9iyZUtZunSpmvnF19c3c0pmAAlJyfJPLJtAiYgMEQCR/MI5Px1z+WacaJqIr3cOyR+YcvJwIiLygAAYHR0tefLkUdc1TVO306I/ju4PgQgNChAvrxzuLg4RETkbAPPlyyfh4eFqEHzevHnVBNi2EBix3d5KEWbFDFAiIg8PgNu3b5f8+fOr6zt27MjsMhkGZ4EhIvLwAFi/fn3L9RIlSkixYsVS1QJRAzx//rzrS+jBOASCiMhA4wARAK9cuZJqO8YB4j5nzZ49W60mjwm2a9WqJXv27En38Tdu3JA+ffpIWFiY+Pv7qxloNmzYINl6CAQDIBGR52eB6n19trAsEoKYM1asWCGDBg2SefPmqeCHhXax1uCJEydUf6Ot+Ph4adKkibpv9erVai7Ss2fPqn7J7CjyXgDkQrhERB4cABGoAMFvzJgxEhgYaLkPiS+7d++WqlWrOvXm06dPl169eqkp1QCBcP369bJo0SIZPnx4qsdjO2qav/32m2X8IWqP2b0PMIwBkIjIcwPggQMHLDXAQ4cOiZ/f/XFtuF6lShUZPHiww2+M2ty+fftkxIgRlm1eXl7SuHFj2bVrl93nrFu3TmrXrq2aQP/zn/9IwYIF1Yr0w4YNE29vb7vPiYuLUxddekM4XAn7iRNhExEZIADq2Z+orc2aNeuhx/tdvXpV1RwLFSqUYjtuHz9+3O5zTp06pTJSsfo8+v3++usvefvttyUhIUHGjRtn9zlTp051yyTd128lSHzi3fUSQ/P4Z/n7ExGRi5NgvvjiC7cNdscCvOj/mz9/vlSvXl3at28vo0aNUk2naUENMyoqynLJqkzViHv9fwVy+Ym/j/3aKRERZfMaYLt27WTx4sUq8OF6etasWePQG4eEhKhmS0ytZg23CxcubPc5yPxE3591c2eFChUkIiJCNalaN8vqkCmKi7uGQHAhXCIiD64BBgcHWzI/cT29i6MQrFCL27ZtW4oaHm6jn8+eunXrqmZPPE538uRJFRjtBT934ioQREQGqAGi2dPe9YeFzNJu3bpJjRo1pGbNmmoYRGxsrCUrtGvXrmqoA/rx4K233pJPP/1UBgwYIP369ZP//e9/alX6/v37S3ajJ8CwBkhEZJBxgLdv31YZjvowCIzDW7t2rVSsWFGaNm3q1GuhDw+D6seOHauaMTGMYuPGjZbEmHPnzqnMUB1moMF6hAMHDpTKlSur4IhgiCzQ7DoGkEMgiIiypxwaopkTEOTQD9i7d281K0u5cuVU8yOyOjGuD7W07AzDINBUi4SYzEzm6bZoj+w8eUU+eKmyvPpUsUx7HyIis4l20XHc6SzQ/fv3S7169dR1zMaChBXUArFA7r///e8MF8Ro9CxQzgJDRJQ9OR0Ab926ZVkQd/Pmzao2iGbKp59+WgVCuouD4ImIDBYAS5cuLd9++60aT4f+OL3f7/Lly1wM9547CUkSdTtBXWcWKBGRQQIgElYw5Rnm4ETmpj5kAbXBatWqZUYZPbb5M6evt+QJcDrPiIiIsoDTR+eXX35ZnnnmGbVCPOb/1DVq1Ejatm3r6vJ5JOsxgPZWziAiIvfLUPUEiS+4XLhwQd1+5JFHVG2QbGeB4RygRESGaQLFLCwTJkxQKaiPPfaYumA9vokTJ6aYocXM7i+DlNPdRSEiIlfVADH59Oeffy7Tpk1TU5PBL7/8Iu+9957cuXNHJk+eLGZnGQLBDFAiIuMEwCVLlsjChQulVatWlm36rCxYmogB8H4ALMwmUCIi4zSBYkX28uXLp9qObbiPrMYAsgmUiMg4ARCZn5iQ2ha2WWeFmpmeBMMxgEREBmoC/eCDD6RFixaydetWyxjAXbt2qYHxWKXd7JKSNbl8M05d5ywwREQGqgHWr19frcGHKdAwGTYuuH7ixAnLHKFmdjUmTgVBrxwiIbmz1xqFRESUwRrgmTNnZMuWLWr19Q4dOsgTTzzhzNNNlQATGhQgPt5On18QEVF2C4A7duyQli1bqvUA1RN9fGTRokXSuXPnzCyfx7EshMv+PyKibM3hKsqYMWOkSZMmcvHiRfnnn3+kV69eMnTo0MwtnQfiEAgiIoMFwMOHD8uUKVMkLCxM8uXLJx9++KFaAQLBkO7jMkhERAYLgFiBNyQkxHI7MDBQcubMqVbkpfsiLRNhcwwgEZFhkmCw/h/mANVh7s9t27ap2qHOeoYYcw+CZxMoEZFhAmC3bt1SbXvzzTct17H0T1JSkpgZ5wElIjJYAORKDw+maRpXgiAi8hAcqOZCN+MS5Vb83Rowk2CIiAwQAH///XeHX/DWrVty5MgRMXPzZ54AH8np5+3u4hAR0cMGwC5dukizZs1k1apVEhsba/cxR48elZEjR0qpUqVk3759YuoxgBwET0RkjD5ABLe5c+fK6NGjpWPHjlK2bFkpUqSIBAQEyPXr1+X48eMSExMjbdu2lc2bN0ulSpXEjLgMEhGRwQKgr6+v9O/fX1327t2rVoA/e/asmhYNSyANHDhQGjRoIPnz5xczs4wB5CwwRETGWw6pRo0a6kKphXMWGCIij8Es0EyoAXIibCKi7I8B0IXujwFkACQiyu4YAF0oUl8KiU2gRETZHgOgi8QlJsnVmHh1nX2AREQGD4B37tyt8ZDI5eg49b+ft5fkz+Xn7uIQEZGrAyDmBJ04caIULVpUcufOLadOnbIsmPv555+LmL35M9hfTQpOREQGC4CTJk2SxYsXywcffCB+fvdrOk888YQsXLhQzCrcMgaQzZ9ERIYMgEuXLpX58+dLp06dxNv7/nyXGBCPGWHMigkwREQGD4AXL16U0qVL220aTUhIELPS5wHlEAgiIoMGwIoVK8rPP/+cavvq1aulWrVqYvYxgKwBEhEZdCq0sWPHqpXhURNErW/NmjVy4sQJ1TT6/fffi1lxJQgiIoPXAFu3bi3fffedbN26VXLlyqUC4rFjx9S2Jk2aiFlZVoJgDZCIyJg1QKhXr55s2bLF9aXxUJqmWcYBsgZIRGTQGmDJkiXln3/+SbX9xo0b6j4zuhYbL/FJyep6aBADIBGRIQPgmTNnJCkpKdX2uLg41S9o5jGAIbn9xM+Hs8sRERmqCXTdunWW65s2bZLg4GDLbQTEbdu2SfHixcWMOAaQiMjAAbBNmzbqf0zzhSxQ2xXjEfw+/vhjMSMug0REZOAAiCEPUKJECfnjjz8kJCQkM8vlkUMgWAMkIjJwFujp06czpyRGGAPIAEhEZOxhELGxsbJz5045d+6cxMffXQNP179/fzHtGEA2gRIRGTcAHjhwQJo3by63bt1SgTB//vxy9epVCQwMlNDQUFMGQD0JhgGQiMhzOJ2zP3DgQHnxxRfl+vXrkjNnTvn999/l7NmzUr16dfnoo4/EjLgUEhGRCQLgwYMH5d133xUvLy+1HBLG/xUrVkytDzhy5Egxm1vxiXLzTqK6Xog1QCIi4wZADHlA8AM0eaIfEDAu8Pz582LWBJhcft4S5J+hLlUiInIDp4/YWPIIwyDKlCkj9evXV5Nhow/wyy+/VKvCm3YZpOAANUaSiIgMWgOcMmWKhIWFqeuTJ0+WfPnyyVtvvSVXrlyRzz77TMyGQyCIiExSA6xRo4blOppAN27cKGbGZZCIiDyTy2Zu3r9/v7Rs2VLMJpIL4RIRGT8AYhLswYMHq2zPU6dOqW3Hjx9X84Q+9dRTlunSTDkEggGQiMiYTaCff/659OrVSw18xxjAhQsXyvTp06Vfv37Svn17OXz4sFSoUEHMhitBEBEZvAY4a9Ysef/991XG58qVK9X/c+bMkUOHDsm8efNMGfyAK0EQERk8AP7999/yyiuvqOvt2rUTHx8f+fDDD+WRRx4Rs0pMSpYrN+PUdSbBEBEZNADevn1bzfcJGO/m7+9vGQ5hVldi4iRZE/H2yiEFcvu7uzhERJRZwyDQ75c7d251PTExURYvXpxqXUAzTYatjwEMDfJXQZCIiAwYAB999FFZsGCB5XbhwoXV7C/WUDPMSACcPXu2ak6NiIiQKlWqyCeffCI1a9Z84POWL18ur732mrRu3Vq+/fZbyWpcBYKIyAQB8MyZM5lSgBUrVsigQYNUIk2tWrVk5syZ0qxZMzlx4oQaaJ9eeTAko169euIunAWGiMhzuWwgfEZhKAWGV/To0UMqVqyoAiH6GhctWpTmc5KSkqRTp04yfvx4KVmypLhLOIdAEBF5LLcGQKwmv2/fPmncuPH9Anl5qdu7du1K83kTJkxQtcOePXs+8D2wXFN0dHSKi6twFhgiIs/l1gCIsYSozRUqVCjFdtxGf6A9v/zyixqUb90fmZ6pU6eqpZr0C9YudBWOASQi8lxubwJ1xs2bN6VLly4q+Nlmn6ZlxIgREhUVZbm4cs1CvQ+QTaBERJ7HrSu4IohhVfnIyMgU23EbWab2BuMj+eXFF1+0bNPnH8XAfCTOlCpVKsVzMF4RF1fTNI0rQRARma0GiEA0evRoNQTh8uXLatsPP/wgR44ccep1/Pz8pHr16rJt27YUAQ23a9eunerx5cuXV1OvHTx40HJp1aqVNGjQQF13ZfPmg0TfTpQ7CXeDL/sAiYhMEAB37twplSpVkt27d8uaNWskJiZGbf/zzz9l3LhxThcAQyDQpLlkyRI5duyYWlw3NjZWZYVC165dVTMmBAQEqFXnrS958+aVoKAgdR0BNavotb+8gb4S4OudZe9LRERuagIdPny4TJo0SQUuBB5dw4YN5dNPP3W6AFhJAqvJjx07ViW+VK1aVS2yqyfGnDt3TmWGZjfhUbfV/2z+JCIySQBEE+SyZctSbcewBGR1ZkTfvn3VxZ4ff/wx3ediOjZ34DJIRESezemqFZocw8PDU20/cOCAFC1aVMwiIuruKhAcAkFEZJIA2KFDBxk2bJhqrsTcn0ha+fXXX9W0ZOivMwu9D5A1QCIikwTAKVOmqGxMZFwiAQbTlz377LNSp04dlRlqFhF6HyBrgERE5ugDRKYlsjbHjBkjhw8fVkGwWrVqUqZMGTGTiGguhEtEZKoAiKnInnnmGbU8Ei5mxaWQiIhM1gSK4Q4lSpSQkSNHytGjR8WM7iQkybXYeHWdNUAiIpMEwEuXLsm7776rBsRj8DnG7WEx2wsXLohZXL7X/Onn46UGwhMRkQkCIObvxJg9ZH5iSrRXXnlFzeJSvHhxVTs0A+s5QJEJS0REnuehplhBUyhmhpk2bZqaHg21QlMFQPb/ERGZLwCiBvj2229LWFiYdOzYUTWHrl+/Xkw1BIL9f0RE5skCxcTUy5cvV32BTZo0kVmzZknr1q0lMDBQzDYLDGuAREQmCoA//fSTDBkyRF599VWHF6U17BAI1gCJiMwTANH0aXbsAyQiMkkAXLdunbzwwgvi6+urrqcHC9QaXUQU5wElIjJFAGzTpo2a/BpLHuF6WjAkICkpSYwsOVnjLDBERGYJgFjxwd51M/onNl4SkzXB8L/QIH93F4eIiLJqGMTSpUslLu5uFqS1+Ph4dZ/R6bW/kNz+4uud/VaqJyIixzh9BO/Ro4dERUWl2n7z5k11n9GF3+v/YwYoEZHJAqCmaXan/8JcoMHBwWJ0XAiXiMhkwyCw5h8CHy6NGjUSH5/7T0Xiy+nTp+X5558Xo4u8VwMMYwIMEZE5AqCe/Xnw4EFp1qyZ5M6dO8UiuZgM+6WXXhLTNIEyABIRmSMAjhs3Tv2PQNe+fXsJCDBnANCTYNgESkRksplgunXrJmZmvRQSEREZPADmz59fTp48qeb+zJcvX7pr4F27dk3M0AfIJlAiIhMEwBkzZkhQUJDlulkXgY2JS5SbcYnqOgMgEZEJAqB1s2f37t3FrPQ5QHP7+6gLERGZaBzg/v375dChQ5bb//nPf1SG6MiRI9VsMEbGOUCJiEwcAN98803VHwinTp1SGaFYDHfVqlUydOhQMUMNkAkwREQmDIAIflWrVlXXEfTq168vy5Ytk8WLF8s333wjRsZZYIiITD4Vmr4ixNatW6V58+bqerFixeTq1atiihpgMFeBICIyXQCsUaOGTJo0Sb788kvZuXOntGjRQm3HVGiFChUSc6wEn9PdRSEioqwOgDNnzlSJMH379pVRo0ZJ6dKl1fbVq1dLnTp1xMjYB0hEZBxO5/JXrlw5RRao7sMPPxRvb28xMs4CQ0RkHBkezLZv3z45duyYul6xYkV58sknxcgSkpLlaszdhYALsQ+QiMh8AfDy5ctq6AP6//Lmzau23bhxQxo0aCDLly+XggULihFduRknmibi45VDQnIxABIReTqn+wD79esnMTExcuTIETXvJy6HDx+W6Oho6d+/vxh9GSQMgfDyMudUcEREpq4Bbty4UQ1/qFChgmUbmkBnz54tTZs2FeMvg8TaHxGRKWuAGAPo6+ubaju26eMDjZwBGsYhEERE5gyADRs2lAEDBsilS5cs2y5evCgDBw6URo0aiVFxIVwiIpMHwE8//VT192Fl+FKlSqlLiRIl1LZPPvlEjN4HyFlgiIhM2geIKc8wEH7btm2WYRDoD2zcuLEYGecBJSIycQBcsWKFrFu3Ti17hOZOZISahd4Eyj5AIiKTBcC5c+dKnz59pEyZMpIzZ05Zs2aN/P3332oGGKPDBOCWJlDWAImIzNUHiL6/cePGyYkTJ+TgwYOyZMkSmTNnjpjBjVsJEp94N8M1lMMgiIjMFQCx+G23bt0stzt27CiJiYkSHh4uRqf3/+UL9JUAX2PPd0pEZBYOB8C4uDjJlSvX/Sd6eYmfn5/cvn1bjI7LIBERmTwJZsyYMRIYGGi5jWSYyZMnS3BwsGXb9OnTxbjLILH5k4jIdAHw2WefVf1/1rD+H5pGdTly5DD4SvBMgCEiMl0A/PHHH8Ws9CEQhfOwCZSIyLQzwZjR/T5ANoESERkFA6ATTaCcBYaIyDgYAJ2qATIAEhEZBQPgA9xJSFID4SGMfYBERIbBAOhg82eAr5fkyen03OFERGSkAPjzzz9L586dpXbt2motQPjyyy/ll19+EcM2f+YJMOwwDyIiM3I6AH7zzTfSrFkzNSH2gQMH1AwxEBUVJVOmTBGj4UK4RETG5HQAnDRpksybN08WLFggvr6+lu1169ZV6wQatQk0jAkwRETmDoCYDQazwtjCdGg3btwQo9GXQSrEAEhEZO4AWLhwYfnrr79SbUf/X8mSJcW4s8AwABIRmToA9urVSwYMGCC7d+9WSSGXLl2Sr776SgYPHixvvfWWGDkJhoiIjMPpvP7hw4dLcnKyNGrUSG7duqWaQ/39/VUA7NevnxhNJCfCJiIyJKcDIGp9o0aNkiFDhqim0JiYGKlYsaLkzp1bjCYpWZPIm3ezXBkAiYiMJcMD4bEYLgJfzZo1Hzr4zZ49W4oXLy4BAQFSq1Yt2bNnT5qPRfZpvXr1JF++fOrSuHHjdB//MP6JiVNB0CuHSMHcnAibiMjUNcAGDRqkOyB8+/btTr3eihUrZNCgQWpoBYLfzJkz1ThDZJuGhobaXZbptddeU2sRImC+//770rRpUzly5IgULVpUMqP/r2CQv/h4c9IcIiIjcfqoXrVqValSpYrlglogVobHGMBKlSo5XQCsII/Emh49eqjXQiDEqvOLFi2y+3gk3Lz99tuqHOXLl5eFCxeqPslt27ZJZg2BYAIMEZHxOF0DnDFjht3t7733nuoPdAYC5759+2TEiBGWbV5eXqpZc9euXQ69BhJxEhISJH/+/Hbvx0w1+mw1EB0d7XD5OAsMEZFxuaxdD3ODplVrS8vVq1clKSlJChUqlGI7bkdERDj0GsOGDZMiRYqooGnP1KlT1SB9/VKsWDGnZ4FhAgwRkfG4LACixoY+uaw0bdo0Wb58uaxduzbN90btEvOU6pfz5887/PpcB5CIyLicbgJt165dituapkl4eLjs3btXxowZ49RrhYSEiLe3t0RGRqbYjtuYcSY9H330kQqAW7dulcqVK6f5OIxRxCUjLDVANoESERmO0zVA6+ZEXND39txzz8mGDRtk3LhxTg+lqF69eooEFj2hBUstpeWDDz6QiRMnysaNG6VGjRqSWTgLDBGRcTlVA0R/HbI1ke2JMXiugCEQ3bp1U4EMYwoxDCI2Nla9D3Tt2lUNb0BfHmDYw9ixY2XZsmVq7KDeV4ixiK4ejK/PAsOJsImITB4A0VyJMXfHjh1zWQBs3769XLlyRQU1BDMMb0DNTk+MOXfunMoM1c2dO1dlj7788sspXge1T2SiusrNOwkSG5+krrMGSERkPE73AT7xxBNy6tQpKVGihMsK0bdvX3WxBwPfrZ05c0aygt7/FxTgI7n8nd5NRERkxAVxMfH1999/r5JfMK7O+mIU7P8jIjI2h6s2EyZMkHfffVeaN2+ubrdq1SrFlGjIBsVt9BMaAccAEhEZm8MBcPz48dK7d2/ZsWOHmAEXwiUiMjaHAyBqeFC/fn0xA8s8oKwBEhEZklN9gOmtAmE0nAeUiMjYnEpvLFu27AOD4LVr18RISTBhrAESERmSUwEQ/YCY/cUM9CQY1gCJiIzJqQDYoUMHu4vUGk18YrJcjYlX19kHSERk8j5AM/X/Xb55t/bn651D8gf6ubs4RETkzgCoZ4GaLQHGy8s8gZ+IyEwcbgLFKg1mYRkCwf4/IiLDctmCuIZMgGH/HxGRYTEA2sFZYIiIjI8B0I6I6Dj1P8cAEhEZFwOgHRFRt9X/HANIRGRcDIDpLYXEGiARkWExANoZ7hEZdbcJlH2ARETGxQBo41psvMQn3R3ywSZQIiLjYgBMo/mzQC4/8fPh7iEiMioe4W1wGSQiInNgALQRca//j0MgiIiMjQEwrSEQDIBERIbGAJjWEAg2gRIRGRoDYBqzwDAAEhEZGwOgjUh9JQg2gRIRGRoDoI3we32ADIBERMbGAGjldnySRN9JVNc5DIKIyNgYAO0kwAT6eUueAIfXCiYiIg/EAGiv+TNPgOTIkcPdxSEiokzEAGiFs8AQEZkHA6CdWWCYAENEZHwMgHZqgAyARETGxwCYRh8gEREZGwOgnVlg2AdIRGR8DIBWOAsMEZF5MADek5iULFdiuBQSEZFZcLT3PVdj4iUpWRNvrxwSkttfzCIpKUkSEhLcXQwiIgtvb2/x8fHJ9PHYDIA2s8AUzO2vgqAZxMTEyIULF0TTNHcXhYgohcDAQAkLCxM/Pz/JLAyA90SYrP8PNT8EP/zIChYsyJlviChbwAl5fHy8XLlyRU6fPi1lypQRL6/M6a1jALRZCd4sQyDQ7IkfGoJfzpw53V0cIiILHJN8fX3l7NmzKhgGBGTOcZlJMLYL4ZqkBqhjzY+IsqPMqvWleI9MfwcPwXlAiYjMhQHQpg+QQyCIiMyBAdAmC5Q1QNIVL15cZs6c6fLHGqXp/Ntvv8309/nxxx/Ve924ccOyDe9bunRplSr/zjvvyOLFiyVv3ryZVoYTJ05I4cKF5ebNm5n2Hmbz9NNPyzfffOPuYjAAApJBzJYF6qm6d++uDoi4oJO8UKFC0qRJE1m0aJEkJye79L3++OMPeeONN1z+WEfonzGty3vvvSeZJSIiQvr16yclS5YUf39/KVasmLz44ouybds2yWp16tSR8PBwCQ4Otmx788035eWXX5bz58/LxIkTpX379nLy5MlMK8OIESPU/ggKCkp1X/ny5dU+wj5z9KQI313VqlWzxT5ftWqV+gxIMqlUqZJs2LDhgc+ZPXu2VKhQQSWqlCtXTpYuXZri/jVr1kiNGjXUSUmuXLnUZ/3yyy9TPGb06NEyfPhwl//NOosBUESi7yTK7YQkU2WBerLnn39eHRTPnDkjP/zwgzRo0EAGDBggLVu2lMTERJe9DzJkMUzE1Y91BD6ffsFBNE+ePCm2DR48OMUJnKs+N/Zp9erVZfv27fLhhx/KoUOHZOPGjWof9+nTR7IaxoCh9qUna2Hs6uXLl6VZs2ZSpEgRFZRwIA4NDX2o90lrMohz587J999/r068bP3yyy9y+/ZtFYyXLFnicfv8t99+k9dee0169uwpBw4ckDZt2qjL4cOH03zO3Llz1QkBgviRI0dk/Pjxqozfffed5TH58+eXUaNGya5du+S///2v9OjRQ102bdpkecwLL7ygatT4+3UrzWSioqIw6lv9rzsREa09Nux7rfJ7mzSzuH37tnb06FH1PyQnJ2uxcQluueC9HdWtWzetdevWqbZv27ZNfa8LFiywbLt+/brWs2dPLSQkRAsKCtIaNGigHTx4MMXz1q1bp9WoUUPz9/fXChQooLVp08Zy32OPPabNmDHDsn/GjRunFStWTPPz89PCwsK0fv362X0snD17VmvVqpWWK1cu9d6vvPKKFhERYbkfr1WlShVt6dKl6rl58uTR2rdvr0VHR6f6bF988YUWHBxsub1jxw71WTds2KA9+eSTmq+vr9qWlJSkTZkyRStevLgWEBCgVa5cWVu1alWK1zp06JD2/PPPq3KFhoZqnTt31q5cuWK5/4UXXtCKFi2qxcTEpCoH9qcO77927VrL7aFDh2plypTRcubMqZUoUUIbPXq0Fh8fb7kf+/25557TcufOrfYHyv3HH3+o+86cOaO1bNlSy5s3rxYYGKhVrFhRW79+fYrPivfWr1tfsM12/8C3336rVatWTX2vKM97772nJSQkpCj/nDlztBdffFG9J74Pez788EP1+7Cne/fu2vDhw7UffvhBK1u2bKr7bX8Ttt+9s/vc1V599VWtRYsWKbbVqlVLe/PNN9N8Tu3atbXBgwen2DZo0CCtbt266b4Xvgv8Jqz16NFD/f4cPUY96DieERwHqJZButf8aeLaH2rAFcfeP0PLSkcnNJNAv4f7KTZs2FCqVKmiml9ef/11te2VV15RtQOcZaIJ7bPPPpNGjRqp5jKcpa5fv17atm2rzlbRjIPxRmk1AaG/YsaMGbJ8+XJ5/PHHVZPVn3/+afexaNZp3bq15M6dW3bu3KlqZzhLRlMd+rR0f//9t+rPQg3j+vXr8uqrr8q0adNk8uTJDn1mNCF99NFHqtksX758MnXqVPm///s/mTdvnho8/NNPP0nnzp1V7bR+/fqqHw37CfsHnwW1l2HDhqn3Re3j2rVrquaB90fTla30+tlQE0NfHGplqMH06tVLbRs6dKi6v1OnTlKtWjVVg0Df3cGDB1UTNmDfYN+jvHjfo0ePqn1nrzkU/XFodsP3gdv4HlGDsvbzzz9L165d5d///rfUq1dP7We9eXrcuHGWx6EWg/2NGjam3bIHr4XmPFuovaD5cPfu3aoJMSoqSj0W7+eMh9nnX331lWoOTg9++2mVadeuXTJo0KAU21CzTq9vNy4uLtWYPPyN7dmzR9Wi9e9Uh3MN/Lbwvb3//vsp7qtZs6ba/+7EAGi1CkQh9v95NByI0OSiN0/hjxLNZehTAQQL/HGvXr1aHRBx0OnQoYNqxtEhiKbVFIamuMaNG6s/8kcffVT9AduDfhsEAcxigb4cQIBF4ERf4VNPPWUJlAgaet9Sly5d1HMdDYATJkxQ/Z/6gWnKlCmydetWqV27ttqGwIj9gMCPAPjpp5+qIITH6dB3ijLipAABEgcs7EdnoU/Huu8LTbQ4WdADIPbfkCFDLK+NAK3DfS+99JLqg9LLnVZzqN7UicCH78MefJ84OejWrZvl9dBXiLJYB8COHTuqprn0YCC2vQCIz4bPgO8U8Dv6/PPPnQ6Af/31V4b3eatWraRWrVrpPqZo0aJp3hcREaH60K3htr3+TOsAuXDhQtVU+uSTT8q+ffvUbQS/q1evqqnLACcEeG/8LnHCM2fOHMtvVYeTJfTj4u8gK8b82cMAaJUBWjiPeSbBtpXT11vVxNz13q6AA4neV4TaGfqLChQokOIxqPWgRgCohaCm4gjUJlFTwMEUfZDNmzdXSQr2ag7Hjh1TQUUPflCxYkV1No/79ACIQGGdWIGDBwK2o6wPzDiQ3rp1K9VBBjUrBD19n+zYscNu7Qr7BEElo1asWKFqXHgd7HfUetFvqUNNAzVPJEPgJAL7s1SpUuq+/v37y1tvvSWbN29W9yEYVq5cOcNlwef89ddfU5xIYOq/O3fuqH2k99XaC2y28HuxNwsJThxQu9bhOk4yPvnkE7vJMml5mHl48T7OvJcrjBkzRgVIZHGi7AiYONH44IMPUgQxlAt/X/gt4KQO3z/+dp577rkUNUcEPwRJd81GxQBo3QQabN4pwRA4HrYZ0t0QXEqUKKGu4w8PAcW6ydG2WcmZPzoEMzTjoIa1ZcsWefvtt1XCApo4bZt9HGX7PHwHzmTFWTeZ4fMCmnVtz/r1GjAeg6Bt2xQF2Fc4EKEMx48fd+pzoCkNTZyoeaGGgOZm1JA+/vjjFM2NqHGhfGiWQ00Mj0ETNAIjnof7EATRlIvnIisyI/A5UZZ27dqlus86mNlrcrQVEhKimqetoYn2999/Vy0MaEK2DrL4TPpJFU4AUBOyhZq2ntWKWmRG9rkrmkALFy4skZGRKbbhdlo1a/1vBsEfrQp4LH438+fPVwEPTe06BEMMVQFkgeJvE9+rdQBE8y++A3dOxejZRzwXzwJj5j5AT4d+BjQ7Dhw4UN1G8wzOVFFDQ03LHtQycHb6oGYwHf5QEUBwQb8Vmq3wnngva0gRR9MOLnotEAdNHPhQE8wMeF0EOjQnoiZiD8qJvjPsD3s1VxyMEIiQ5o5amW2AQPnt9Ukhm/Cxxx5TfanWTYe2ypYtqy74jpB9+MUXX6gACNhPvXv3VhdkGS5YsCDDARCfEycr+gH4YaD2jO/OGpo6n332WbWfrOHz4D49AKKvEk2Etvbv36/uA9S6M7LPXdEEWrt2bfX7x1hKHU7u9Cb0B528PfLII+o6gj4ysNNrxtRretaQbaq3TrgLA2CKlSDM2wTqSfCHhOCGM26chSKJAGeX+CNE8gOgKQ1/yOirQPMMDryXLl2yJL6g+Qu1ECTFoCkOfThotkMSjPVZvQ59dXg/HHDQhIZkEwREHPht4b3Rn4VaEZpN8bqoMSIwOdLslhE4A0e/G4ILDjbPPPOMqn2gKRA1ETRTIWgjsCD4oD8MB180neIAhn4c9NXgQFy3bl3Vv4k+RpwkoPw4MCKBBWfytlCLQeDF66B5F/t47dq1KZoR0f+H4QKooWMVEvSFoqkTcABGWjy+I9S20EyLk4iMGjt2rPotoJ8W74kDM5pFccCdNGmSU6+F4IQaKr577B/0daEZF/vmiSeeSPFYPG769OlqeAD6BvFdoPaFpljURvEaX3/9taoxo09Ml5F97oom0AEDBqjfJGrbLVq0UN/f3r17VY1Oh5ORixcvWsb6oa8YNV/8HeC7wufFfrUeBoK/RfzO8XeFv1X8TWGf4bNYQ9JQ06ZNxa00k7GXPvvkhM1qGMSRiw+XUutJ0ksxzs4wDEJPgffx8dEKFiyoNW7cWFu0aJEaBmANQwowVKFIkSJqqACGMHTq1Ek7d+6c5THffPONVrVqVTW0AcMl2rVrZzeNHSn/SBHHcAUMIXj66ae1rVu32n2sM8MgrOH5eB1Hh0HYpshjqMbMmTO1cuXKqc+LfdOsWTNt586dlsecPHlSa9u2rRpygCEL5cuX1955550UQ1EuXbqk9enTR5UF+wUp+vgseN+0hkEMGTJEDSPBMAcM58Bn0cscFxendejQwTKEBN9H3759Lb89XC9VqpQasoAyd+nSRbt69ardz4r/9eEPae0f2Lhxo1anTh31GfGd1axZU5s/f36a5U8Lhk6gvHg9WL16tebl5ZXiu7RWoUIFbeDAgZbbmzZtUkME8uXLp/YPhoJYfx/O7PPMsHLlSjWEA+/5+OOPW4afWP+91a9f33Ibxwz8vej7FUOSjh8/nuI5o0aN0kqXLq2G4uBzY+jE8uXLUzzmwoUL6jd6/vx5tw6DyIF/xESio6NV+zvOjnFmHJeYJOVGb1T37R/TRPLnyrzFF7MTJAQgSxFn5Jm11AiREaCGtm7duhQDuenhoJUFNUjr2qYzxyjb43hGmb4J9PK9ZZD8fLwkX2DGkhmIyLiQaIK+OIz9y+qsS6MKDQ1NNQbRHUwfAO9Pgu3PtfGIKBUkDFkn+NDDe/fddyU7MP1coJZlkPKYdwgEEZEZMQByFhgiIlNiADT5LDAmy4EiIg+hZcGxiQHQpAvhYkyTPlUWEVF2g2nrIKMzLXlMEgzSjDGtFAY3YzJizKeX1kTDgFnYMScdZoHHIFxM7YS5GR+qD9Bk06ChYx8Duq9cuaJ+YO6ajJaIyLbmh+CHeXExC45+sm7IAIhJdJEOiyVcMLsAZs7A7AuYysjeIpf6Io76zB/Lli1Ts31geiHbmRkcYdZZYJDxinn8MM7G3rRVRETuhOCX3rykruD2gfAIepg+CUu1AKZxwryAmAcQS5rYwppqsbGxag01HWYmx4SrCKIPYj2AMnfuICk35gdJSNLkl2EN5JF8rlvR21Ngf7MZlIiyE7RKpVfzM8RAeBx4MVks5pvToSkOcylivjxXLOKIueisJ2HFjtNduxWvgh+G/4UGmasP0Hp/cyYYIjIjt3b8YAFFTBDrzKKMzi7iiKZSnCnoF+s12vTmzwK5/NVMMEREZB6GP+qjdolqsn7BEjW6CmF5ZPfIRvLV6+kvKUJERMbj1iZQLDaJdl5nFmV0dhFHrJGmLwhqy9srhxr+YLYhEERE5OYA6OfnJ9WrV1eLMiKTU0/KwO2+ffu6fBFH0HN+rPsCiYjIc+jH74fO4dTcDOtEYR2wxYsXq7Wf3njjDbVWmb7eFtYGGz58uOXxv/76q1oH7qOPPtKOHTum1lXDulKHDh1y6P2w/pS+nhwvvPDCCy/isZf01hN0hNvHAWJYAwZjYxVnJLJgOANW+NYTXbDStPUg7Tp16qixf6NHj5aRI0eqgfDIAHV0DGCRIkVUPyCWNcFYOJxJIDEG2x4mndaouH8ejPsofdw/D8Z95Nz+Qc0Py1PheO7R4wDdzVXjSYyK++fBuI/Sx/3zYNxH7tk/hs8CJSIisocBkIiITMn0ARBDJMaNG5fmUAmz4/55MO6j9HH/PBj3kXv2j+n7AImIyJxMXwMkIiJzYgAkIiJTYgAkIiJTYgAkIiJTMkUAnD17thQvXlyte4cFePfs2ZPu41etWiXly5dXj69UqZJs2LBBjMyZ/bNgwQKpV6+e5MuXT12wduOD9qcZf0O65cuXqxmH9LlujcrZ/XPjxg3p06ePhIWFqcy+smXL8u/MxsyZM6VcuXKSM2dONQvKwIED5c6du0u4Gc1PP/0kL774oprZBX8vaa3vau3HH3+UJ598Uv1+SpcuLYsXL3b+jTWDw1yjfn5+2qJFi7QjR45ovXr1UnONRkZG2n085hr19vbWPvjgAzU36ejRo52aa9To+6djx47a7NmztQMHDqi5WLt3764FBwdrFy5c0IzK2X2kO336tFa0aFGtXr16WuvWrTWjcnb/xMXFaTVq1NCaN2+u/fLLL2o//fjjj9rBgwc1o3J2H3311VdqjmT8j/2zadMmLSwsTBs4cKBmRBs2bNBGjRqlrVmzRs3xuXbt2nQff+rUKS0wMFAbNGiQOk5/8skn6ri9ceNGp97X8AGwZs2aWp8+fSy3k5KStCJFimhTp061+/hXX31Va9GiRYpttWrV0t58803NiJzdP7YSExO1oKAgbcmSJZpRZWQfYb/UqVNHW7hwodatWzdDB0Bn98/cuXO1kiVLavHx8ZpZOLuP8NiGDRum2IaDfd26dTWjEwcC4NChQ7XHH388xbb27dtrzZo1c+q9DN0EGh8fL/v27VPNdDpMrI3bu3btsvscbLd+PDRr1izNx5tt/9i6deuWJCQkSP78+cWIMrqPJkyYIKGhodKzZ08xsozsn3Xr1qnly9AEiknvMZH9lClTJCkpSYwoI/sIk/7jOXoz6alTp1QTcfPmzbOs3NmZq47Tbl8NIjNdvXpV/VHpK0vocPv48eN2n4MVKew9HtuNJiP7x9awYcNUu73tj9HM++iXX36Rzz//XA4ePChGl5H9g4P59u3bpVOnTuqg/tdff8nbb7+tTqQw24fRZGQfdezYUT3vmWeeUSsfJCYmSu/evdUKOCRpHqcxafbt27dVv6kjDF0DpMw1bdo0leSxdu1a1bFPopZo6dKli0oWCgkJcXdxsiUseo3a8fz589WC2FgSbdSoUTJv3jx3Fy3bQIIHasVz5syR/fv3y5o1a2T9+vUyceJEdxfNUAxdA8QByNvbWyIjI1Nsx+3ChQvbfQ62O/N4s+0f3UcffaQC4NatW6Vy5cpiVM7uo7///lvOnDmjMtqsD/jg4+MjJ06ckFKlSomZf0PI/PT19VXP01WoUEGd1aO50M/PT4wkI/tozJgx6kTq9ddfV7eRjR4bGytvvPGGOlmwXiPVjAqncZzGUkmO1v7A0HsRf0g4w9y2bVuKgxFuow/CHmy3fjxs2bIlzcebbf/ABx98oM5EsXBxjRo1xMic3UcYPnPo0CHV/KlfWrVqJQ0aNFDXkc5u9t9Q3bp1VbOnfmIAJ0+eVIHRaMEvo/sIfeu2QU4/YeD0zeK647RmcEg/Rjrx4sWLVbrsG2+8odKPIyIi1P1dunTRhg8fnmIYhI+Pj/bRRx+pNP9x48YZfhiEM/tn2rRpKp179erVWnh4uOVy8+ZNzaic3Ue2jJ4F6uz+OXfunMoc7tu3r3bixAnt+++/10JDQ7VJkyZpRuXsPsJxB/vo66+/Vin/mzdv1kqVKqWy1I3o5s2bamgVLghL06dPV9fPnj2r7se+wT6yHQYxZMgQdZzG0CwOg0gDxog8+uij6sCNdOTff//dcl/9+vXVAcraypUrtbJly6rHI9V2/fr1mpE5s38ee+wx9QO1veAP1sic/Q2ZKQBmZP/89ttvangRggKGREyePFkNHTEyZ/ZRQkKC9t5776mgFxAQoBUrVkx7++23tevXr2tGtGPHDrvHFX2f4H/sI9vnVK1aVe1P/Ia++OILp9+XyyEREZEpGboPkIiIKC0MgEREZEoMgEREZEoMgEREZEoMgEREZEoMgEREZEoMgEREZEoMgEREZEoMgJSmxYsXS968ecVT5ciRQ7799tt0H9O9e3dp06aNmBEmXMbkylm1ugG+jxs3bqT7uOLFi8vMmTMztSzOvoer/g4c+T066+jRo/LII4+oibLJeQyABocDPP7wbC+YjNjdcGDRy4OJf/GH3KNHD7l8+bJLXj88PFxeeOEFdR0rNOB9bNfomzVrlipHZnrvvfcsnxMTGmNCbASea9euOfU6rgzWWHkBnx0rC1i/vl5OTOBcunRptbAv1qJ7WFjgFd9HcHBwukHljz/+yLKg7AkmT56s9l1gYKDd/VWxYkV5+umnZfr06W4pn6djADSB559/Xh18rC8lSpSQ7ADLl6A8Fy5cUGvo/fDDD2oZGFctmeLv75/uY3BAzopa7uOPP64+57lz5+SLL75QK2m89dZb4i4LFy5UB9bHHnvM7m/lf//7n7z77rsqeH/44YcP/X4IqPg+EFzTU7BgQXWwp7uwPNQrr7yS7m8FJ41z5851yYmK2TAAmgCCAA4+1hfURHDWiHXGcuXKpWolWJU7JiYmzdf5888/1bI+QUFBKnBhiZe9e/emWAm9Xr16aj0uvF7//v0f2DSDAyLKg1XlUVvDc7DGIFZ1xpIxqIGgZojPULVqVRU4rA8Offv2VcvoYEFeHMynTp1qt8lJD/jVqlVT25977rlUtSos0IpyWC/TA61bt5Z//etfltv/+c9/5Mknn1TvWbJkSRk/fvwDDz5YCxCfs2jRotK4cWN1UMPyLTqsGN6zZ09VTuy/cuXKqRqaDoFoyZIl6r31WhqaFeH8+fPy6quvqkCeP39+VV7UeNODhYyt1yy0/a1gX+Kgi7KuW7dO3Xf9+nXp2rWr5MuXTwUpfF8IlLqzZ8+q18T9+E0h6GPFd9smUFzHQTsqKsryWfD5bJsnsSo6Fsu1hlXjsb7e0qVL1W18V/jO9f1WpUoVWb16tTjD0b8D/JbKlCmjvvdmzZqp/W4tI7+LB8FrDBw4UJUvLU2aNFGtCTt37nyo9zIjBkATQ7Pjv//9bzly5Ig6uG7fvl2GDh2a5uM7deqkghGaqfbt2yfDhw9XC5vqC8Gi9vDSSy/Jf//7X1mxYoUKiAhQzsBBDAc1HDgQAD7++GO1+C5eEwcdrK2nH3RRdhycV65cqRaa/eqrr9QB1J49e/ao/xFcUcPBCtu2EJT++ecf2bFjh2UbDiwIuvjs8PPPP6sgMGDAANX/8tlnn6nmPDRVOQrBadOmTSnWvsNnxr5dtWqVet2xY8fKyJEj1WeDwYMHqyBnXZtHDQ4BAfsFJyUo26+//iq5c+dWj8MJgj34THgPR9ZyxPehvw5OFnDCg32+a9cutS5d8+bNVRmgT58+EhcXJz/99JNaE/H9999XZbGFciPI6bV/XPD5bGGff/fddymCEfYb1spr27atuo3gh2CI1eTxO0aw6Ny5s1PBwJG/A7wnvmO8F/YxAnmHDh0s92fkd4GTMOzTh4XfEU4OUQZykotWs6BsCsuIYJ2sXLlyWS4vv/yy3ceuWrVKK1CggOU2lhcJDg623Mb6ZFjPzJ6ePXuqNc6s/fzzz5qXl5d2+/Ztu8+xff2TJ0+qZahq1KihbhcpUkQtk2PtqaeeUsvCQL9+/bSGDRtqycnJdl8fP++1a9eq66dPn1a3scZYeksV4fq//vUvy+3PPvtMlSMpKUndbtSokTZlypQUr/Hll19qYWFhWlqwVBT2A/Y9lrbRl3rBmmfp6dOnj/bSSy+lWVb9vcuVK5diH8TFxWk5c+bUNm3aZPd19TXXsC5fWvsCr7dlyxa1XNHgwYPVd4PnYL1M3dWrV9X7YPkwqFSpklrCJ73lbvTlfGy/e+vltmbMmGFZEigkJERbunSp5f7XXntNa9++vbp+584dtSYcllay/S3icWmxfg9H/w5Qduvli7AGHbbt3r3b4d+F9e/RkXUkraW1v3Rt27bVunfv7tBr0X0+zgZM8jxotkQfgQ5NPXptCGfQx48fl+joaFXrunPnjjrbtdcPM2jQIHn99dflyy+/tDTjlSpVytI8iloaamE6/M2jZnP69GmpUKGC3bKhGQy1BDwO7/3MM8+o/imU59KlS2r1cGu4jfcCnD2j+QfNhajxtGzZUpo2bfpQ+wq1jl69esmcOXNUcyA+D8709dW58d6oAVif2aP5Mr39Bigjak543P/93/+pZJx+/fqleMzs2bNl0aJFqp8QTcCoeeHMPj0oDxKaUAO0hvdBrdwevDagqc7W999/r74P1OrwnaAZEs2TWH0bzbi1atWyPLZAgQLqcx07dkzdRvM1mk03b96sfh9oDahcubJkFN4PtV58B+gXRnM6mhnRfAv43Njn+A1Yw35DU7ejHPk7QFmeeuopy3PKly+vmpzx2WvWrJmh34XejOsKqKnjfcg5DIAmgICHjD7bZjgEDByw8EeLviM0WaIfCgcQe3+wOBDigLh+/XqVrDJu3Dh1MEJzFJqp3nzzTXUQtPXoo4+mWTYcuPfv368CDPry8IcMOBA9CPpbEFxRFhzEcLDEgdfZPiBr6MNC4MZnxAEPzUozZsyw3I/PiX6Zdu3apXquvYCi07MqYdq0adKiRQv1OhMnTlTbsB/RDIgm39q1a6v9guST3bt3p1telAd9sdYnHtYJJfagD03v07N9jH6yhPKiPxQHfkfh5AjNsdh3CIIIKvg8toHe2ROS+vXrq8xg9Jni94GTHdCbRvF+6Fu19qDkp4f5O7Ano78LV0Gztn4ySo5jADQp9OHhDB8HKL12o/c3pads2bLqgr6W1157TWU0IgAiGKHvwzbQPgje295z0D+EAzDOqnEA1OE2zritH4dECVxefvlldXDEwQAHMmt6fxvOytODgxUOYggoqGGghoPPpsN19Dc6+zltjR49Who2bKgOvPrnRN8YEjB0tjU4fAbb8qM86G8NDQ1V+8IROFDisfi+8F0+6GQJUINHzQgBGeUE9JdiXyAVX4ckkt69e6vLiBEjVGavvQBo77PYg/fCa+Iz4kQHrQ56vzPeF4EONWbr30hm/B3gs6P/U//t4XOjH1Bv2XDV7yKjDh8+rH7/5BwmwZgU/lDRzPXJJ5/IqVOnVLMmEgnSgmYzJLQggw/ZfjhgIxlGPwAMGzZMfvvtN/UYNO8hUQXNVc4mwVgbMmSISqTAwQ8HFyTd4LWRaKBn73399deq6erkyZMqgQQZjPaGNSBAoPaAhJbIyEjV9JperQO1CjRH6skvOiSnoOkKZ/tImkATGGpvCGjOQC0PzYNTpkxRt5FdiAMskjzwWTBIHfvXGhJ80MyMfXH16lX1/aF8qNEh8xO1VdSI8R2hJo6hJfbgQI+aMmo6jkL58B5oHsbz0OSHZBPUvLAd3nnnHVV+lAG1eiQTpdX0jc+CWhOaVvFZ0mu+Q6sDfpuoAVp/H6glo9aMkzEkr+CEAe+L3zRuu/LvAEEXgRwnAAiaaH7H+Ds9IGbkd4GkGZwkpAfBHb95/I8TBlzHxToxCLXYixcvqu+UnGTVH0gGZC9xQockDHTSI5GhWbNmKtkgrUQFJFZ06NBBK1asmObn56cSQ/r27ZsiwWXPnj1akyZNtNy5c6uEj8qVK6dKYnGmYx+JJ0iqKFq0qObr66tVqVJF++GHHyz3z58/X6tatap6rzx58qhEhP3796eZdLBgwQJVfiSk1K9fP839g/fFfsHz//7771Tl2rhxo1anTh213/C+NWvWVGVJLwkGZbf19ddfqyQTJKMgoQNJDNgfefPm1d566y2VIGH9vMuXL1v2L8qGxBIIDw/XunbtqhJG8HolS5bUevXqpUVFRaVZpg0bNqj9qif3pLUvrF27dk0lbqCM+m8GyTE6/B5KlSqlylCwYEH1WCTK2EuCgd69e6tkE2zHPkorQeXo0aPqMbjPNuEJt2fOnKkSgfAbwfuiXDt37kzzc9i+h6N/B998843at/h8jRs31s6ePevU78L294jfIPZ5enC/njRlfdG/e0DyDcpNzsuBf5wNmkTk2fBnj4QWvSmbPBP6KVE7X7ZsWaqEMXowNoESmRAGn2PgP2cP8WxoGsV4UQa/jGENkIiITIk1QCIiMiUGQCIiMiUGQCIiMiUGQCIiMiUGQCIiMiUGQCIiMiUGQCIiMiUGQCIiMiUGQCIiEjP6f/z4g+M6jVuWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import RocCurveDisplay \n",
    "RocCurveDisplay.from_estimator(random_search.best_estimator_, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that this is a simple problem, we have obtained very good results, with AUC of 0.95."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us first plot a very simple tree only of depth 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0.75, 'x[7] <= 0.051\\nentropy = 0.952\\nsamples = 455\\nvalue = [169, 286]\\nclass = 1'),\n",
       " Text(0.25, 0.25, 'entropy = 0.314\\nsamples = 282\\nvalue = [16, 266]\\nclass = 1'),\n",
       " Text(0.375, 0.5, 'True  '),\n",
       " Text(0.75, 0.25, 'entropy = 0.517\\nsamples = 173\\nvalue = [153, 20]\\nclass = 0'),\n",
       " Text(0.625, 0.5, '  False')]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth=1, criterion='entropy').fit(X_train, y_train)\n",
    "plot_tree(tree, class_names=['0','1'], filled=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the 8th feature was the most informative feature for the single split (the split looks at x[7]). Let's see what that feature is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mean concave points'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns[7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of looking at splits for the whole tree, which can be difficult for trees of higher length, there are some useful properties that we can derive to summarize the workings of the tree. The most commonly used summary is feature importance, which rates how important each feature is for the decision a tree makes. It is a number between 0 and 1 for each feature, where 0 means \"not used at all: and 1 means \"perfectly predicts the target\". The values of feature importance always sum to 1. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.72035249, 0.        , 0.        ,\n",
       "       0.01248399, 0.        , 0.        , 0.        , 0.00128767,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.0127925 ,\n",
       "       0.05448777, 0.06153931, 0.05364876, 0.        , 0.00495046,\n",
       "       0.        , 0.        , 0.06839799, 0.        , 0.01005906])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance=random_search.best_estimator_.feature_importances_\n",
    "importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize the feature importances using matplotlib's horizontal bar plot *barh*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,8))\n",
    "n_features =X.shape[1]\n",
    "plt.barh(range(n_features), importance, align='center')\n",
    "plt.yticks(np.arange(n_features), X.columns)\n",
    "plt.xlabel('Feature importance')\n",
    "plt.ylabel('Feature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that the feature used in the top split, *mean concave points*, is by far the most important feature. This confirms our observation in analyzing the tree that the first level already separates the two classes fairly well.\n",
    "However, if a feature has a low *feature_importance*, it doesn't mean that this feature is uninformative. It only means that the feature was not picked by the tree, maybe because another feature encodes the same information.\n",
    "In contrast to the coefficients in linear models, feature importances are always positive, and do not encode which class a feature is indicative of. The feature importances tell us that *mean concave points* is important, but not whether a high value of this variable is indicative of a sample being benign or malignant. We will study in more depth feature importance when we discuss model interpretability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision tree for regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In decision trees for classification, Gini index and entropy are used as a measure of impurity to determine which feature split maximizes the information gain. However, in decision trees for regression, the impurity metric used is the mean squared error, and the predicted value is the mean of the target values of the data points associated with the leaf."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate the use of decision trees for regression we will use the Ames Housing dataset. We will download the dataset directly using the pandas *read_csv* function, and instead of providing the file path as the input parameter, we will provide the URL. Since this dataset has 81 features, we will load only one feature and the target for illustrative purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gr Liv Area</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1656</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>896</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1329</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2110</td>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1629</td>\n",
       "      <td>189900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gr Liv Area  SalePrice\n",
       "0         1656     215000\n",
       "1          896     105000\n",
       "2         1329     172000\n",
       "3         2110     244000\n",
       "4         1629     189900"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['Gr Liv Area', 'SalePrice']\n",
    "df = pd.read_csv('http://jse.amstat.org/v19n3/decock/AmesHousing.txt', sep='\\t', usecols=columns)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's use the *DecisionTreeRegressor* to model the relationship between the target, SalePrice and Gr Living Area variable. We will not split the dataset into training and test as we should do for actual application.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor(max_depth=3)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor(max_depth=3)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "X = df[['Gr Liv Area']] \n",
    "y = df['SalePrice']\n",
    "tree = DecisionTreeRegressor(max_depth=3)\n",
    "tree.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of nodes is  15\n"
     ]
    }
   ],
   "source": [
    "print('number of nodes is ', tree.tree_.node_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QvR63eI0Wdd/dNtrn3a1FT0hICH8JQmWkvHyjSmIHUJEFh4zvEYTSp1xrqtD5h7omLbt37+YuPYCUHcTM4sWLHaI9qJXq2rUr38ftuXPnuKtPsWTJEo6Cod5JbYOOwNzcXPs26BRs3ry5vdMQ22iPo7ZRxzGzFkEQiu8b5eluPCt2AOrYEBwzV+6l6fO28u3JtMLOXl93D7enYX/azIX8X6zYQ/M2pPDt3dOX8ONqG0EQfDRSNXr0aOrWrRun//7xj3/QunXr2OZAWR3gl+CoUaPo5ZdfpqZNm7Kwee6557gLD3YHKrJ1zTXX0AMPPMBpQwinRx99lLvxsB244447uLYJdgnjxo3jFOO0adNoypQp9rWMHDmSevXqxZYOgwYNoq+++or9sqysRRAE3/ONqgzzAss6DSsIlZVyFVWXX345/fDDD1x79NJLL7FQmTp1KvtOKZ588km2OoDvFCJSPXr0YMsEGHQqvvzySxZSffr04a6/m2++mf2ktF16CxcupOHDh1PHjh2pevXqbOKp9bKCuJs5cyY9++yzXDQP4QTLhcTEREtrEQSh+L5R5eFwXtEFh4zvEYRK4lNV2RCfKqEy4Qu+Ub6wxpKSX1DAqUyk+twxtFfTwvE9/uU+bEMQvAqf8KkSBKHi4gu+UeVd91UW+EoaVhAqAiKqBEEoNZRvVHFHz5Q2lUFwyPgeQagkNVWCIFRsvN03ylfqvjwxexFF9+5SnPhMfFE4CoK3IKJKEIRSpSS+UaVNZRAc2jSsUTG+Pg0rCELxkfSfIAhlghXfqLJck7fXfVWGNKwgVBSk+68Mke4/QfA+lE/V1DnJLucF+rq7ukqzHk/N9Lo0rCBUlOu3iKoyRESVIHgnlUlwVORRPIJQ3tdvqakSBIEq+wXeWd0XHofPk7/m1tcFCKdfL74HXyu6FwRvxyOiCu7i0dHRntiVIAiVAG8d7qsEB3f5+fvbI1fesj5BELwby+m/1157jRo2bEhDhgzh+5jZ99133/Gw4Xnz5lHbtr45yqEskPSfIFyqYZryc3KRWXuQKfZZexdFS1lHsqysT4SVIFQO0krLUR1Di+vXr8/fL1q0iL/mz59PAwYMoLFjx5Zs1YIgVHggRiBY0N6v/4sO95dsPUw7Dp1lwYJIFkasTJ+3lW9PpmUVbleKpaDu1ofHUdQugkoQhBKn/44dO2YXVXPmzOFIVb9+/Th61blzZ6u7EwShEmFmuO+oQW2oeb0YmvjT5iKRIvhJ2SNFpeBxJcOHBUEoCZYjVTExMXTw4EH+fsGCBdS3b1/7L6P8/PwSLUYQhIqNu1l7LepFsxnntDnlEyny9VmA/Hu4oMDhVhAEL45U3XTTTXTHHXdQ06ZN6fTp05z2Axs3bqSEhITSWKMgCBUEd7P2Bl/WgI6cOV9ukSJfnQXorYX/glDZsCyqpkyZQo0aNaKUlBR6/fXXqWrVqvz40aNH6ZFHHimNNQqCUEFwN2uvc9Na9OO6A6YjRbf3SPCoLYAvzgJUhfUTZ28p83SpIAglEFW5ubn0r3/9i5577jkWVlpGjx5tZVeCIFRC3M3aqxIaWK6RIm+eBejM0wu3EFRGc/1UuhTLxFBrQRC8qKYqKCiI7RMEQRBKY9be+aw8y5GislxfecwCVO/RqBMSQgteWmbSpSdSM6XGShC8rVD9hhtuoB9//LF0ViMIQoXH1XDftXuOs6BxJ1WsRIqsFm970/Bhe2rvp800bPoS+mLFHpq3IYVv756+hLJy8326sF4QqLLXVKFA/aWXXqLVq1dTx44dqUqVKg7PjxgxwpPrEwShggEhhEs70lF39WrmMGuvQ+PqFFs1lIWVUTpLHykqjeJtV+sr61mA7lJ7IUEBPllYLwgVFcuO6vpaKoed+fnR/v37PbGuCok4qguC+zoh28XHYZuAtJXe0fzqi5EiV47mnnJFL8/ZhMozCxEqZ7+kv32iHxf2I3LljqG9mhYW9vtbTlAIQqUnrbQGKh84cKCkaxMEQXA63Bf/L2mkyFPF2+U5fNiMZxbSpX3a1PPKwnpBqIx4ZKCyIAiCJ1GCqWZkKN3RM8EhUqR9viK7opvxzPr5z7856uaJdKkgCOUkqg4dOkSzZ89mr6qcnByH5yZPnuyBZQmCIBQvUmTVFd3TXldl6Zm18/A5fq9Ih+ItuEqXigGoIHihqFq8eDFdd9111LhxY9q5cyclJibSX3/9xf9gO3ToUDqrFARBqOCu6MX1zJo2N5l6tKzjFYX1glDZsSyqnn76aXriiSfoxRdfpIiICPatqlmzJt155510zTXXlM4qBUEQKrArujvPLFepvava1KPwkMBip0sFQfAclttAduzYQXfffTd/HxgYSJmZmTyqBjYLr732mgeXJgiCUPwIjye9rsoLq55ZnC7193e4FQTBiyNV8KVSdVR16tShffv2UevWrfn+qVOnPL9CQRCEUojw+ELxtjd5ZgmCUAqiqkuXLrRq1Spq2bIlDRw4kB5//HFKTk6m77//np8TBEHwlghPRSjeLkknpCAIXm7+CXPPjIwMSkpKovPnz7Oo+u2339hpHZ1/DRo0KL3V+jhi/ikIZYcSTJiNJxEeQRDK4vptWVQJxUdElSCUPeXpii4IQsWg1BzVBUEQfInydEUXBKFyYUpUxcTEmP7L7syZMyVdkyAIgiAIQsUUVVOnTi39lQiCUKFSbMAb0m6S/hMEwatE1bBhw0p/JYIg+BxKpGDWHorBz53PpgHt4ymhTpS9QBzu5jDj7N+ubAvE9Wsrr3UIglB5CDRboGUWKcAWhMoBRAuEycTZW3h4MfspDU6iBjUjaOJPm+2PKTBuBd5RsDqA63BpChqjtZXHOgRBqFyYclSPjo7muipXX2obK7zwwgv8C0371aJFC/vzWVlZNHz4cKpWrRq7tt988810/Phxh31gqPOgQYMoPDycx+WMHTuW8vLyHLZZtmwZzyUMCQmhhIQE+uSTT4qs5e2336aGDRtSaGgode7cmdatW+fwvJm1CEJlAlGgKT8ns8EmREuLetHsZD5tzqXHtOA+Hp86J7nUhYx+beW1DkEQKhemIlVLly4ttQXAjf3XX3+9tKDAS0saPXo0zZ07l2bNmsWtjI8++ijddNNNtHr1an4+Pz+fBVXt2rXZK+vo0aM8QicoKIheeeUV3ubAgQO8zUMPPURffvklD4T+5z//yW7w/fv3522+/vprGjNmDL333nssqFBDhud27drFQs3MWgShMoEoENJqiAIpBl/WgI6cOe/wmBEw4xzaqxnViAwtFVGDtWXm5tO/+rWiMdcl0fmsPFq75zj9/OfftPPwuTJbhyAIlY9y9alCpOrHH3+kTZs2FXkOXhA1atSgmTNn0i233MKP7dy5k53c16xZw+7t8+fPp2uvvZaOHDlCtWrV4m0gjMaNG0cnT56k4OBg/h5iaOvWrfZ933bbbXTu3DlasGAB34eQuvzyy+mtt97i+wUFBVS/fn167LHH6KmnnjK1FiOys7P5S5tGxX7Fp0rwdfILCmjmyr30xYo99se+faIf/bjugMNjzhjaqynd3iOB59OVRh3VsbMXOBql6qiQ7qsTE861VVPnJlN+ga1U1yEIQuX0qSrWbxIIkkmTJnHEB19TpkzhAxWHPXv2UN26dalx48Z05513cjoPrF+/nnJzc6lv3772bZEajI+PZyEDcNumTRu7oAKIMOHNb9u2zb6Ndh9qG7UPzDHEsbTb+Pv78321jZm1GDFhwgT+ENQXBJUgVAQgXCBYtFQJDSzymDPOpGd7fJCxvY7qp810z1tLWdzN25DCt/e+tZQf792mHo0a1KZU1yEIQuXFsqj6888/qUmTJiyk4EmFL4ynwWMbNmywtC9EiFDfhIjRu+++y6m6nj17Unp6Oh07dowjTajV0gIBhecAbrWCSj2vnnO1DYRXZmYmD4FGGtFoG+0+3K3FiKeffprFpvo6ePCgpfMjCN4KBAwiQFqQZtM/5gyMi8E+yrqOavrcZK77al43utTWYRYIQET8tLeCIPg2lh3VUVt03XXX0QcffGCvf0JhOCJWo0aNohUrVpje14ABA+zfY5YgRBZmB37zzTcUFubd0+PNgMJ4fAlCRQMCBuIEnXRKCqBuqU+beg6PGYG4EObveTJCZFTjZQTqqO7o2ZTrv3bPPufxdZhBrB4EoeJSrEgV6pS0BeX4/sknn+TnSgIiQc2aNaO9e/dy8TlSc0g1akHHHZ4DuNV34Kn77rZBThTCrXr16hQQEGC4jXYf7tYiCJUJXPRrRYVxrZICheB1Y6s4PGbE1W3jWDx4UjhAqECguIv14PnFyYeoS7NapbIOKynKYdOXOKQo756+hB9X2wiCUAlEFcSIqnvSgtRWREREiRaTkZFB+/bt4868jh07chcfuvUU6MbDsbt27cr3cZucnEwnTpywb7No0SJeY6tWrezbaPehtlH7QFoPx9Jug0J13FfbmFmLIFQ2cPGH11O/tnEcfUJnHYTNyGsvPVY0QhVHo65tUyLRYJQ2M6rxcgbqqFD/VdJ1FAexehCEio3l9N+QIUPo/vvvp4kTJ1K3bt34MdgKwB/q9ttvt7SvJ554ggYPHswpP3TwPf/88xw1wn5Q2I3jwOogNjaWhRK68SBiVLddv379WDwNHTqUXn/9da5vevbZZ9lPSqXdYKWArj5E0u677z5asmQJpxfREajAMeAaf9lll1GnTp3YUuH8+fN077338vNm1iIIlQ0IBDb8vK4t3dWrGQuq3UfOUePakQ6PnUV6KyKEU20lSW+5S5sN6hBPv2w6aO/scwbWooRYWYoXKylKsXoQhEoiqiCm8A8dflDKZBNRnIcffpheffVVS/s6dOgQC6jTp0+zZUGPHj3o999/5+8BiuHRiQejTVgToGvvnXfesb8eAmzOnDl8bAicKlWqsDh66aWX7Ns0atSIBRRqwaZNm0ZxcXH04Ycf2j2qlFCEBcP48eNZmLVr146L57XF6+7WIgiVEXXRrxkZSnf0TCgUWhcjSDU0j2nnARZXULlzSEfkCZ19k37eYqqeq6wFi5UU5cLNBwutHspRVMnMREEoQ5+qCxcucKoOoPMPjuaCZ3wuBMEXURfe0pr5h3ojpMecgdQiImQjPlpNu46cc7lNeYD3P33eVq6hcgeibo8NTCwXEVPan6MgVOTrt+VIlQIiCh5RgiD4Hp6OQpTmrD2rabN/Xd2SHv90jcMacEQUpqs6qvISK1YtJ8o6UiUzEwWhZJgSVRjHYpbvv/++JOsRBMEH2/mxT1yIjSJJqgAbu7UaJVLiz2ra7NMRV3m0nqu0bCjKynKivD9HQagsmBJVCHkp8Evphx9+4MdQ2K0cx2E3YEV8CYJAXhWFQJTn9h5NLEewSqMAWyv+UK9lprMPA507Nq7hUOOF46iidCrn6IrWhsJVGlNZPZQ1UkgvCGUkqmbMmGH/Hh5V//jHP3jGHgrFARzJH3nkEakTEgQvxlkUIsDfjwu8EUU5evYCX1StRLA8XYCtF3+znujnMm2mXf+xcxd4JqHR+r3JhgJvH+LEm1KUvlZILwjeiOWaqo8//phWrVplF1QA38NuABYLb7zxhqfXKAhCKUYhIEgwEw+F4MWpo7HqEeUuraUXf+6c2kuyfpwX/rp43NLucjOyofCWFKWnP0dBqIxYFlWwUdi5cyc1b97c4XE8BtNMQRC8D2dRCKTMEOFx1llnpo7GkwXYRuIPTu0QRkZps+KuX8374y63c5m8TVl1uRnZUJTUcqKyFNILQoUTVTDEhBEm7BRglAnWrl3LHlXKLFMQBO/CWRQCM/COnDlfojoaTxZgG4k/rVO7Pm1WnPXb7BEqoslzttCh0xl07WUN6IZOjdhpHYOht6WcoRZx0aXa5Yb9KlHiDeLEFwrpBaFCmn9i3t2kSZPo6NGj/BjGysBR/fHHHy+NNQqCUEpRiM5Na9GP6w6UqI7GkwXYzsTf1LnJfItoE9awOPkwp816tKhNs9bst7Z+f38smibP3kxJDWLpievbsjDDeVDRKrwXbOcttVhlgbcX0gtChRRVcBXHyBd8wQwLSIG6IHg3zqIQiMx4oo7GUwXYzsQfRs/AKX3O+hSOTiGqVDW08NeX1fVfyMmjcxnZlBgf67YWC2uGBqssURlvLqQXBF+g2OafQMSUIPh2FAKpLk/U0bgqwMYxa0eH04XsPAoPCXR5QXaXgoJT+q7Z5/gC/9mIq6haRIjl9SOlt/mv0zSgQ3yJaskqIt5cSC8IFV5UCYLg21EId511VupocKHFMWpEhNAtXRtTSFAAi7bfdx+nV77byMOW3XUSWk1B4QJvpQ4I+w8OCqAGNSNKXEtWUfHWQnpB8AVEVAlCJY5CnEnPorqxVTxWR+PKkZtMRH+Ug7qVFJQVEYZBz6BxzQjrtViVTEx4WyG9IPgCIqoEoRKhogyIJinH8ezcfBYpJa2jKYkjt9ZBfdHmg9S+UfVC8XdFU1q4+ZDLFJRZEYb043e/76eOTWpQy3rR4skkCILH8bNVpvYWH5lyLQilDYTIiXOZdM9bS8lf40iOlJjqrIOI6dMmjurEhDukf9wNaM7IyuO0IvylYIdgxNBeTe2deMpBfcrPyQ4F483rRnNReo+WtSk0KMClMacSWcdTMw3rgJL/Pk1PfbGW8gps7G01+Z6u7Lz+xYo9bs+Vdq2CIFRO0kxevz0mqv7880+6cOECXXHFFZ7YXYVERJVQ3mjFDyJUq3Yes4sfJWK6NKtl92tCPVReQQH1a1toMaBH7UuJGa0lAcQYHoMdArr3tAzqEE+PDUy0CyRnBeMKHN9Mwbj2/anbL1fupc+X73bY7uPhV1JggD8Nm77EbS0WCuL1NVVGx5FaI0GouJi9fnss/Td06FDavXs3zwEUBMG70KbX9OIHX0r8TJy9uchrIYCuaVff8oBm7BeGnQB2CM468Tw5xFdbB6T2/cVFQYUIFUQjvLkgGrH2ScO60v9+3eE0oqavJXN2HkvbhV0QBN/AY6Jq8eLFlJub66ndCYLgIUoiflxZKbgqStdbEsBfCnYI+k7C0hziq/atT29qTT4hhqbd171IRM2olszMeXTV2SgIQsXHY6Kqbt26ntqVIAgepLjix5WVgpWi9Dt6NuUIEfyl9NEfCJDSKhhX7uxmBi5DPDWuFUnzNqQ4LYi3ch4FQaic+FsZpJyd7fjL7/jx4/Tiiy+yu/qqVatKY32CIJQAiALUO5kRP0fPXmDxo0UJIH3kxUqEaXHyIa7TKhRol6I/aj9WzTvNgm2b1YniCNW0OcksemxOxNC0ucmUUCeK67wQDUOaEaj3beU8nkjNLPF4G7we9g/aW0EQKpCoeuCBB2jEiBH2++np6XT55ZfT22+/Tb/88gv17t2b5s2bV1rrFAShGBRH/AAjAWRmRp+zCBNGyqDgG1EcbVG3clB3F38qzhBfbNsrsa5pk0+IIYCC/JKISKQprYg//XEAooDoTpw+byvfnkzLKty/iCtBqBjpv9WrV9Nbb71lv//ZZ59xUfqePXu4In7cuHH0xhtv0MCBA0trrYIgWMSq+EEBNywE3I0kKU6ECSNl7F1zF6NApTnEF/sOCfRniwgrNVtk0NVn9TwWx9dKarYEoRKJqsOHD1PTpk0dCtNvvvlmFlRg2LBhNGPGjNJZpSAIxcKq+MFFHcICF22knfw1t9oLubsZffoIU3ZeAX23Zr9htxxeX1pDfIsjhuBdpV9ncUSkVRdyqdkShEokqkJDQykzszA8Dn7//XeOTGmfz8jI8PwKBUEoNlbFjx/5cbec3ndKbxlgNcI08qPVtFNTAO8Qebm4P08N8dV7SFkRQzAu1RqCqnWOGVzYPeiJGYnO1uxJawlBELxcVLVr144+//xzmjBhAq1cuZKL1K+66ir78/v27ZMOQEHwMpT4wYW4VnSY3aPpvM71HOKnemRoYfrpJ3PpJzPjYUYOasMiSSuoXEVeSjLE18hD6tqO8VwbZkYMwT0eZqdG66wbG85djKWRplRrLy1rCUEQvFBUjR8/ngYMGEDffPMNHT16lO655x6qU6eO/fkffviBunfvXlrrFAShGEAk5eXb6M4rmnJ3n9ajSRl/bk05TS3qxVhOPxkNaNZHmJT/k9nIC9cvaQSStv6qOPVI+4+nsQ+VGTEEB/gJ3280fP6zZbvphk6NvCpNKQiCD4uqXr160fr162nhwoVUu3ZtuvXWW4tEsjp16lQaaxQEwQRGo1Nwi+uvK48mCIWsnHw6lW49/aTEgz7ChEfnb0hxKaj0kRe8prhu5c4EIaJw2BfMTV2JoREXI2pajy79On9Yt59u757gsTSllrKo2fIEMp5HEDxo/tmyZUv+MuLBBx+0sitBEDyEu9EpGCa8ZGvRDjht9AkRlu/XHih2+kk7Hga3uPjuPppqOvKCWq7idL5pL/L/6teK2jWqVmSQM4RdZHgwiyGk8LB/qxE1cDot26mItJKm9ETtW1lHqmQ8jyCUsaM6UoIYUxMfH++pXQqC4KERNHAVNxpBAxC9GTM4yXL6ydWF1ErkpVndKC6ORy2XmdQjuhFVQb27WYYYO4OvjKxcOpmaSVtTznAaT9WVhQT5m4qo6SNEehFZ0ghPaVpLlBSxehCEchBVKFqXgcqC4DsjaLTbZefmW0o/ZeXmU1hwoEciL71a1zXtVo7asPDgAAoPCaLJP5mfZYgCfdSTaTv7wNjr21LbhtV4HaURIbIS4TFT+I+IojYyVhaI1YMglIOoghnohQsXPLU7QRDcUJL5e3oOnEg3LYLQJbdqxzEWI85a+61EXoID/U13vm06cIquaR/PNWL6/baoF83vEQIqwN+P+iTVo+gqIfTlyj0cmTKKxCFVqKJb2v1p94XXQnSGBgVYisRYjfBwFOti4b8+TYlzjkJ6pHJb1Y/hfZVFVEisHgShnEQVRtYIguC9I2iuv7yR023+PplOLeNiLHXJHU+94LK132zkxUrnW4MaEUWiWhBQSG9CFGIkjbbDEXYK6P7LyctnR3c9+kL2JcmH2QbCaF9W64eKE+FR5qMQpNo0JawecM4RacR7KquokFg9CEIpiaq0tDTDx6tUqUIBAQEWDysIQnmMoDECl8D2jarT4TPn3YogbZdcQu1Il2koM5YLqtbIbOqxUc0ImrVmv8PaIKh6t6nnssMR4u3Gzo3oi+VFI3HzNx6k1vGxvM3oa5MuenVtLlH9UHEiPACv+WL5bpcipiyjQmL1IAilNFA5OjqaYmJiinyFhYVR8+bN6YMPPrB4aEEQSoLVNnxEPIyAWKoVHU5v/LiZi7ohgj4efiXddUVTGtQhnmcBzni0Nz++NPmwvahbFW67Qt8t99jARI5mKBGh0l5mhyqHBAU4XOSRpsNrp81J5siPsw7HaXOTqUpIEIs7bYTr8cFJNOXebrzvhZsOcfH7FDf7mjon2ZRvltUBzGU1tLm0rR4EoTJjOlK1dOlSw8fPnTvH/lVjx46lwMBAuvfeez25PkEQPFAMjgiL3i1cH33acfgsrdh+lNo2qEbbDp51mn4qTuG2q245rN1s/ZX+Io+6J6TpzEaEbu/RhI6fu8D39RGuJ65va2lfriJFxY3weFtUyNutHgTBZyNVMP80+rr++uvppZdeosmTJ9Obb75Z7IW8+uqr/Atq1KhR9seysrJo+PDhVK1aNapatSoPcMZ4HC0pKSk0aNAgCg8Pp5o1a7K4y8tz/It82bJl1KFDBwoJCaGEhAT65JNPihz/7bffpoYNG/IMw86dO9O6descnjezFkEoLZBOgpWA9lZbDO4KiJHa0eHUoUl1h+jTZyOu4ujT+n0n7dEnFG7XiAqjzX+dplsmLqQBL8/j24mzNzt0DqrWfk+kn3AhRgE26ppQL+RneMEuTOGdTs+mPm3q2bdBIfni5KIeXM6iO3CXx3ueOapPkQiX1X25isoUJ8LjjVEhKz9jnvp5EIRKIarcAYG1d+/eYr32jz/+oPfff5+SkpIcHh89ejT9/PPPNGvWLFq+fDkdOXKEbrrpJvvzsG+AoMrJyaHffvuNPv30UxZMGKmjOHDgAG/Tu3dv2rRpE4u2f/7zn/TLL7/Yt/n6669pzJgx9Pzzz9OGDRuobdu21L9/fzpx4oTptQiVW+CU1r7UhRO1NjNX7qXp87by7dnz2fbXoMbHlRhB4XXahWyKrRLCqTek4G7u0pjSM3PoVFoWdWlWi6M2SIdpC7fdCZySvG/t+0fh+VNfrOXUoqvUI2wc/jqRTnVjq9gv8s66+pxFd4IC/TnqEl01hAvttVEpq/tyV09mNqWpIjzFeU1ZoBoOyuLnQRB8HY91/6WmplJUVJTl12VkZNCdd97JNVkvv/yyw/4++ugjmjlzpn1w84wZM9jR/ffff6cuXbrwyJzt27fTr7/+SrVq1eJROf/5z39o3Lhx9MILL1BwcDC999571KhRI5o0aRLvA69ftWoVTZkyhYUTQJTtgQcesKcu8Zq5c+fSxx9/TE899ZSptQgVH0+6SpvZFy5R+pZ81ekWWzWUZ/mhqw9F5lwMfkVTWrj5Uhs+hAciVIdPZ1DN6HCaZNDar1KDWl8nFbUqjXEsRucB+88rsPGx4aWFlJ5R6rFT0xrcwajt1sPzVqI7sEa4rUcTKijAcR3PhdV9uRoVU1wzT280ADXbcCBRKkHwkKiCk/obb7zBaTOrIKWGSFLfvn0dRBXqtLBfPK5o0aIFO7avWbOGhQxu27Rpw4JKAaH08MMP07Zt26h9+/a8jXYfahuVZkSUC8d6+umn7c/7+/vza/Bas2sxIjs7m7/cdVAK3o8nXaVN78vPjyb/XJiectXp9vnyPdS8bqGv0i1dG7OfUm5+AS3fdpRmrtxDYwa3NfR1cmYQuvvIOUpOOUNXtalHNSJCPDqOxV3tEVKMzry00G0IoaUVfRdy8ujqpDjTNT84N+oBfVRq7Z7jnFr0VP2QWUuJ4hiAlrWIKa3xPIJQaUWVs1QXojgQMPhHtXLlSksH/+qrrzjdhvSfnmPHjnGkCV2HWiCg8JzaRiuo1PPqOVfbQOBkZmbS2bNnOY1otM3OnTtNr8WICRMm0IsvvmjqXAjejSddpa3s6+CpjCKdblqBpDepRLRl+/Gz1Lp+LNdHXXe5+UJuuJU/eUNbjtagy04/UsXT/kNW7RRUpAkX9XaNqvNjEEnhMeGmozt5+QXcvYc5gfrjOjMCdbav0ojweHtUyOp4HkGobJgWVc5Se/Xr1+eibaTwrKT/Dh48SCNHjqRFixZxcXhFBNEv1GopIORwvgTfwpOu0lb2hXTetZc1oJ0XIzfaTjdXhpeq3mjcDe14kDCeM1N8jf2i5upkWhYt+r34ppdWUp8ZmTmWIk0Qe3f0SLCnRguFGbmN7qCuDMdFBA9Dl3ccOlskKqU3AvVEpKg4ER6JCglCJRBVqCHyJEipoRAcXXkKRIxWrFhBb731FheSIzUHywZthAgdd7Vr1+bvcavv0lMdedpt9F16uB8ZGckeWzAuxZfRNtp9uFuLEeg2xJfg23jSVdrKvhAt0bqgq/l1NguGl/BdslJ8jQv2PW8uNZXedDck2Ezqs3m9aHY8dxcd6t8ujqpHhvIxjhvUoUHIuoru7D2aSvM3pvDYGhwLrvBAf1xtahGfI7oBPREpKk6Ex1NRoeJ8ToIglHOhulX69OlDycmOk+FRKI5aJRSaI6ITFBREixcv5kgY2LVrF1sodO3ale/j9r///S+LM9gpAES+IJhatWpl32bevHkOx8E2ah9I63Xs2JGPc8MNN/BjBQUFfP/RRx/l+3je3VqEiosnXaVL4oKuutOM0oCuUojN6kSR478A1wahNjf7K07BvvbCnp1XGC06eDrDdHQIXlpm6tBq6KI7Ow6do/9+u4F2auwg7KJzcBsWntrj5l8smJ+7PoUevLqlT0eKPNlYIQiCl4uqiIgISkxMLDLyBj5Q6vH777+f02exsbEslB577DEWMaowvF+/fiyehg4dSq+//jrXNz377LNc/K4iRA899BBHvp588km67777aMmSJfTNN99wd58Cxxg2bBhddtll1KlTJ5o6dSqdP3/e3g2ItKa7tQgVl+L4B7mKVBXHBR1CCvVA/7q6JQUHBbCBpdl0ZK/EuuwobmZQst4g1Gh/1SNCTBfsO7uwq9olPPbm/K1uo0PArIhMzcyhqPAQmuKmbi3An2jUtUmmolK+Vj/kycYKQRB8QFSZAbYH6MRDdAhddOjae+edd+zPI203Z84c7vaDwIEogziCGakCdgoQUPCZmjZtGsXFxdGHH35ot1MAQ4YMoZMnT7K/FYQZrBkWLFjgULzubi1CxcWTrtJ4blDHeE4/aYvL0XmGQmlEbrT7wqBjjFLB8bNy8ijA35+fiwoPZgdw7WtcpSOtDEp2htofPK7MiBszF3atlYPWTqFqaKBdkFmpQ0OxPewScE7dbf/LpkN0W4+mlHIqg9Iu5NhtHHJy8yk4sNDCz1cFhycbKwRBMI+fTRzbygwUqiPqhY5JRLsE38JZpEQBE0R3FyklFOAxhYu+NnIDUYPIDep6UESNfcHcE0BMwSRTH+3RvgapKz0w0ITZJ3ygps9Ndl58PagNR2ogblyB/T06MJGd1p2Bfc4c3Ye9tMyesxEfrXZwbFfrVgXpOGcZBuJTD8xCb+ueQP+3ai99sWKPy/eitr+1axPKySuwC9zAAD/uKvRVQaVE6LDpS9z+EQBX/bIYzCwIleX67dWRKkHwFjzhH2Q2clM3Npxa1IuhA8dTqUHNSI68uCpI10Z79CCVBTGyZtexImmuZnWj6IpWdez2CV2b16ax/n4uRYurwcwKrBH7z823mYou3dGzKUeolD8VOhsHdozn83jCQEiqtKGRkEQdGl5vpW4NUalvftvnUHMUFhzoszVHnmysEATBGsUSVSjYxhcKxFHUrQUu5IJQ0fCEf5CVlExuXj4LKjYAnWPuNUifaaM92jqpIm7lIYHcGYjo13ca+wRXosXZYGYjMErm2zX7TV3Y4Qqv7XJE1KxhjYhiCUl8HlhzdFggndr9J+1b9R2lHd1PttyswnfgF0j+Af58zgJDqlBm957045Q0Wrp4CeRI4ecUHEZVqlSlju0SqfeVV3JHMjp/UTpw4403UoMGDdjoGOl/TGdA2QFGW6ET+PTp01SjRg2qV68e9ezZk0sU1OQI1H5ilJeq0YR5MMDv0Tp16lC3bt143NbRo0epevXqPFYL9zFrFK+98sor7c9je+3+FehSxhzT75esoxN5VSm+00Dy8w+gs39vo+z0sxQSEUNR9ZrSwT9/ocwzx+i7lJZ0a+f/UIBBlzK6sTE3FV8Ax8cx3a3BaD/wMLTymvKgOOv0xHvztvPjbevxtXVbTv/BzBI1SyjqxsL1F5EffvjB02usMEj6r2JQnBZ1qykZzOWLrxFBZ9KzaJjO4sDoNZiPtzXlDA8+dpdaQ40W3NJhgulqbA1m8WlFi7P9GbHg2YE8p3DehhRyhzal2OKixUJx0obqPMyd/SM9+tD9lJ0FIVV+QIShjhPD4o0MjvXgAoALgzPwM6b9da32r4yZ0YyDkVuO+/Aj/+AQKsjJcnlcNOGg2Ufx/fff04MPPsgiUQvqSrV/SOvXoAf7gR/hoUOHTL+mPCjOOj3x3rzt/Hjberxp3Wav35ZFFYQU/vHhLyfBGiKqKi+ojcIgZLN1PojchIcEWH7NLRMXOtgQ6IVRcUQLxtao9GZ2bgHd9PovbkXenGcGWKprwtpvnbiQJt3TlapHhpkSn3ohiTU3yttntz2p6Cgh/+233/IMUowKKwljx47l3+24QJk9h9o16C9e2M8tt9xSZNCyq9eUB8VZpyfem7edH29bj7etu9REFSwPYLjZpEmTEi+ysiGiqvKCf2ZWIzf4lWD1NZj1p9KRRim8sde3pVZxMXTf28vcipZPHutN57NyKSIs2CG9aUaQjRmcxKlFvZGou+NUiwgxJSQhDkcMTKQGNSI4jYnxNQF+NoqrH08njzsfHVXRwOdRt25d7lp2FeUyAyJW6enp1KxZM4e/+M2sAVGBAwcO2NMtWEvDhg2d7sfoNeVBcdbpiffmbefH29bjjes2e/0u7Bu2wD//+U+aOXNmiRYnCJWN4vhToSPN1WsgLCCSvn2iX2GnnM3GI1zgPI4IEyJUTetE2beZ/+xAurJ1XS5UNzu2BqIFa8D6VaE9CvYn39OVWtZznIVZaANxsWAfkwiiC2fyuQIRMGzXsGYEd6G5M0dFETrSl4i2VQkJ4mjYm/O2cv3WnF8WVypBBfB5HD58uMSCCmAfiFZZEVRqDRg7pp39iu9d7cfoNeVBcdbpiffmbefH29bjy+u2XKielZVF//vf/+jXX3+lpKQkdhrXgpy+IAjF97pSxeXwojJ6jau5f8ot+9rL4mmwLZ6u1m2D6I6VzjgcC3/t6e0ccJyp93Xn8S8LMP6latGCfYgwvWO59n2qFGXy36epVf0YfhyvgQN86+vbGvp4ISLnbDzPsS3LLX4qgp49e9yna52BAmGj782+pjwozjo98d687fx423p8ed2WRdWWLVvYHBNs3VrohKzwxfZjQSgL8G+jVlSYJRNO/HMymotndu4fXq/f5r6rWlh2h5/ixpX7kQGFUTKjMS7+FzsTYZuAfaiOSQhHI18uPxvRgA7xhkOiVdTLWfoxOKJQmAnFp2nTprRw4cJivRb1tkbfm31NeVCcdXrivXnb+fG29fjyusX8swyRmqrKjUqfoevOVeRGW1z+0pDL6LKEGvbXqAHExTXVtFJThS5E+EQ9/ukat8dx9Z7Pns+mDftOUZdmlyJPiMQh8qTt3Pv+yf4UEuRPU3427kqcNKwrVY9yXsRuK8inFZPuo+x0x461ioy311QhNWl0ifGWGp3irNMT783bzo+3rccb111qNVWCIBQPRHLwBRHy6Yir6K4rmnI6C91vEDB4HIIKkRvFi7PW0+n0LPtrkL47dtGN3RUQYHBthy+VFggZeEiZqXVCKu+DRTvcHgfCC232+KWGLkftLzd8P299CnfooTMRtgm4nbP+b7ru8gb2Wq/vxvan8JBAFlQQi0ZDnWEx4crUEn5MLQb9iyoLKiI4ffp0tkQoKdhHWFgYt6FbXQPmpWovWvhe7UefwXD2mvKgOOv0xHvztvPjbevx5XWbElVoR4RKU9+7+hIEwTnqH3rNyFC6o2cCF5jD0RpF2uh4Q4RK262H7+95axmdycji18AU00h0ODPVRHRIC5zSIUzgQ4Uokz5hby82H9SG66V2uvGjUq7cKKpHpyI6906mFXoiqZSgtoZLW2jetXktCgooXAFczS9k59Kh0xlOj4Uol7t6sFqtulHb256hwGBzKc7SpH79+vTdd9/R5Zdfbu4Ffq5/HesvGvgLXLWLwwoBReb6iwd8pWAe6gq8RtkpAOwP60antx7sz9ka9OAxPAcjVLOvKQ+Ks05PvDdvOz/eth5fXbep9N+9997Lfw1FRETw966YMWOGJ9dXoZD0n1Dc1CC67WrFhFNslZBimWqSk0J3RL0g0vTu8FjP2/O3sgu71eMoA1Ee64P6K41FwhODk6h3Uj0K9Pfnuil0Iqq6KdUJ6GwEDaJaqLMy4311Z4/GVCPrL5o4cSJt3LiRHc2xu3xbgIOjenTjthRRkEF/71hPubm5/Fr824QQad68OfXu3ZuWL19eao7qcLNftWk3O51H129B5w7uZOfzoPAISj+2n+8HBIXRDbfcRlOfHEZr1qxx66iOde3bt49tbx555BHeRus0jbW///77DtsEBwcXOYfiqC6O6t6wHm9Zd6n5VAnFR0SV4ArVNacXOn3bxFGNqFCOAmHEjFlhoTUENaJ53WgaOeiS1xNqnU6mXaDGtaIoJy+fvl69z/RxbuzUmNbsPubQsZdyKp1a14+lrNw8ys2z8eN4j/iNY9XNvTj1YNpBwZ4Yhl3ehrA8o08XKRIEoWyQgcqC4GOoMSSRVYIL5/Npirr7JNXjiA6sBdApZ8WawRlwSg8PCaKlW4/YXckhXBAtwpBlCBx3xwn096MbL64VgkfbsQehAjDV5Kc/DlBQgD8N6Z7gVOC4m2WIejDVBaheD68u1I3pxRyibXxsm43TkWZq0Ib2auYgxEpjXFFxbTYQQVTdlYIgeC8iqgTBi8BFOTw4sIjwQG0UhIqRsHBnzWBlm+7Na1NYSCDtP5ZGDWpWpXnPDnTwiUJNlpbXhnam0OAAt8OPa0aGcTQMKT8zAgcWDBBLu2afK1IPBruIAH/iKJgzry5tStVVcbu+NoyjQW7EixJQmOWo9+4yM1i7ODYbSiQKguDdiKgSBC9AH/UYM7gN1Y0Np8+W7eYLvjZCpQrNXZlqQnigI69BjaockXJm34B9IRqEOqvRg9pwhApAKCE1pfeJUvVOBQU2uvvKZpQYX8105Ol8di59//sB00X2SF3q2XbwDK995LVJvAZXYg41Xe4c2vVmp+6iQUqoTXTj3YUknRlhhf1x7Zmbz9KsUBMEoXwRUSUI5YirqAeiNbd2bcwF28FBAbwdLtrKcgFCBZEVFHvb66+SCou9D53KoNiIUN7mrl7NeN/OjDfthqJJ9QoFw0/OBQMu8I1rR/KImNrRYey0bjbyhO2tCByk87Qpvu4talNoUID9nKHmy52Yw3qtmp26ilThuBBUZkSkGbA/vM7oc9I71AuCUMFFFUbWhIaGem41glCJMB318Pejv0+mc8ecckqf/PMWrjmC2FD1VxgqDGuCtbuP0/iv/+RIBy7Ut/dowvYNuIBnXKzRQspP1StBtCCNBsxGndbuOUExVUNMp9YQebqte4IlgZOTm09T7ulKrerH0vFzF3i+n1Z03nlFU66BMuoUVGLu4f6tONpjpm5pYId4Fi8oIDeqk8JndaIU6rP0NhvaY2ufFwShAooqpBT++9//0nvvvUfHjx+n3bt3U+PGjem5555jZ1O0CgtCZcVK8bKVqEejmpG8b9QPaaMaOw6dpeOpFwyjT9jH58t3syBRkZP3F24vcjwIM6TmUs/nmI46pV3IoZBAf2upNX8/0wLn6qQ4CgzwZwd5d/VaQN8pqN7/iu1H6Zr28S7rlpD6fH1oZ4qtGmo441BFiwpKoT7L4X37+dm3t/I6QRB8WFS9/PLL9Omnn7JR3AMPPGB/PDExkZ1LRVQJlRGrxctWox5Ie8FxvHZMOEeiqoYGuYw+GQkh1CAZDThG5xwu4kgjWql3QnTITOQJomVgx3heK1KTZgqza0WH8/fF7RRU7D6SSv3a2lzWoEFQtYyLcVuf5efh+ixBECoelkXVZ599Rv/73/+oT58+9NBDD9kfh/ndzp07Pb0+QfB6ilO8XJyox18n0qlJ7Sjq3KwWjfxoNU26pyst2HSQggL9uTsQtgtGnXpaIbQk+XCR+p2q8I+CELAgGPg1NpspSwDUa8EJHqIlqUGs2yJ7FNAfPnOe7xe3U7BInRSc3K9ryylDvEbVLV3TLp6qR4aaFm/N6kSRo5Vq8euzBEGoeFgWVRhcmJCQYJgWVI7EglCZKE7xcnG60hrWjOA0HS7T425sx48N6BBfxFJA36mHiJIq/OYxODYb9W8b51C/k5tXYKneKSs3n5ZtPcLHdxV5UvVaSrQs2XrYaZG9KsyGiOzarDa/p5J0CiqvLtSjYd4h1turdR37+76Qk8c+W2aL7REx7JVYl6bNTRZfKUEQPCOqWrVqxVbwGNWgBTN22rdvb3V3guDTFLd4GULGiohBEToiUvCwgtt5zegwt5162noj7ANRLLDnSCpd0+7SRR8RtAIWIOZNRVftOMaCjUfeuIg8PXh1SwfRAoE3yaDIHmuD9xSKxLETM3P+jDoFjXy4vl69l26tFcnrhSCCKemIa9twB2NefoHliKH4SgmC4DFRNX78eBo2bBhHrBCd+v7772nXrl2cFpwzZ47V3QmCT1OcNJ6fimaYdNOGiNl/PI1axMWwKAkKLLRXMJuy6t6iDguW9Kxcnp+H1B3XgF1MRyrD0fDYQMumolp7B70lAIrNkVqD35X+PaL+SZ+uwyiWm7s05vMCsWNFdCrBaOTDlZtfYH8e60iMj+VvcP7GXJdkOWJYFr5SJXVrFwTBR0TV9ddfTz///DO99NJLPEgUIqtDhw782NVXX106qxQEL6U4aTxEtjbsO+k2daYVMZv+OkXN6kbTrDX7eNSL2ZQVaojCgwOoSmgIpWbmFnEeV0X0EAe4dRV10huGaiNPc9en0IS7OtE/ujVhW4ecvALuvFPjdcyeH5iPXsjOo5Agf1NjclSq7UTqBR7s7JBG3HSQI1MfPnKlfVyPPh35r36tLPtY4TMsLV8pT7m1C4LgQz5VmP68aNEiz69GEHwMq2k81CINm76EhUJSw2qmRUzHxjU41TakWwLZCsxHxzKycql6RKipzjb+z89mWO9kZNmgZeeRc/T92gMcacK+xn72OwsvNV7H7PnJzM7nVB7WBkFoNtWGVN5jAxNZ5O06fI4fi64SwvtQkbX+7eJYRCEK9vh1SXzus/PyTds8qDqp0vKV8rRbuyAIPiCq/vjjD077de7c2eHxtWvXUkBAAF122WWeXJ8geDVWhuLioohaJLUdhM7ke7q6FTEYzYJjLNiYwp5L8Lm8pWtjFgyu5vIhKpNQO8p0mhCLRI1WyqkMe70TUoUQgli3M8sGbaQJ1g/a0TdpmTmW0pypF7IpPTOH/bVQf+auU3DkRdEJM9QZj/amrSlnaNLszfTpY70pMT6GOjWtSYdPZ9CTN7SluGpV2bPr+82F0bpqESF0S5fGpm0e9HVSnvaV8rRbuyAIPiCqhg8fTk8++WQRUYUaq9dee43FlSBUFqwMxcXF+5XvLg0v3n7oLNf74GIaFhxQaBbpD0dvG51IzaQpP29mAYVo1aJNB6lNg2r8upMwqNx8yGW3H4AwOnbuguki+piqwfYIiap3Qg0WUoZfrNhjzgU9r8Bh9A3ODUw8zaY5tfMBtfVaeisE1GvBywpF+9hOW+ulBAg6AiEoEd1RNVTaCNDjg5MoIMCfkv8+bWqWYmmm3krLrV0QBC8XVdu3b+caKj3o/MNzglDZMDMUV0VUtJEeCCiMnoHTeE6+jf5v1aUBxnjN6MFt+WL7K/bpRyxQrLiLw9TTrC2BKqJH3ZZ2jdpBzvr9qJl8OA5SdgUXxWDTOlH8vKpdgjeVmTQn6p46N61pWIMVERrEcxAxAxHja3DOAQTb1Un16NGBjudXdQT6++M8+xeJ1mlrq2DzAAHoapZiadcylbZbuyAIXiqqQkJCeDwNRtNoOXr0KAUGynxmofLBNTWqA+6KphxF0qfxkMLT1yLhQm7GFiEyPJgNP626i1u1JcD70JtoIq2oImHq2GyjMKgNixK9RxYiSNPu687mnXgO7wvfQzhhbTDqxGP684MUHeqvELlTNVg85LlNPUMhqdKpo69tQ6OuTWKxpj2/qiMQ8waR8tNHgPA+1fqwX2c2DxB6KLyPqWKuLqwsGx4EQfA+LKugfv360dNPP00//fQTRUUV/kV67tw5euaZZ6T7T6iUqALjL1bu5WiSqkXKziugM+lZXBi9Yf8ph6G/+i40V0JpzOAkOnr2giV38QZ/V+WCdqteWFe0qkNT5myxrxV1WoigaNNjrsSOtqAawaQnrm9LkWHBvP6DpzLosoQaXA+GLj8lWlJOplOHJjV4f+0aVePI2Pp9J02fH4g1FKV/PeZqB8+rrSlnqUPj6hwB1EeAjKJ4RjYPyuqhtCNDVhsexK1dELwTNJJYYuLEiXTw4EE2/+zduzd/NWrUiI4dO0aTJk0qnVUKgheDqMGUn5O5uHri7M00ZPIiri1C0TaAozciOH5OIiWugIjJK7AVES+u3MWvTKzLQgMF5hAm7i69Wi8sRGV+HNefxl7floUfQARo6cXxNp+PuIr3OW1OMosamxOxM3VOMqfm2jWsTh2bVOeU2ouz1tPiLYcpLDiQjp29wKLmbEYWR+HU/hAZg/v5A31bmj4/SDm2rh/D+3tz3la+xTxEFKkjqmYUASpOFK8kQHTD2FR7a9TwYOazErd2QahAkap69erRli1b6Msvv6TNmzdTWFgY3XvvvXT77bdTUFBQ6axSELwUowLj8bd25Iu5iuQ0rxfNKTFtCg2RklU7jnIkR9UkOZvbh1EqVgQAtgfvL9xOY65ra8kLq1GtSBY9EEPa4neVHntxyGXskXXodAYLL7V2ROOgE+D6rt7HqbQs2njglN2rypmb+vHUS1E4FRm7qk09wwiTsxojFKVri+ntEbNr2xhGgLDGsogMmfWdstLwIG7tguC9FKsICqafDz74oOdXIwg+hrbAGELq+Vs7Fql/0qfQMNS4Skig6bl9iDhZFQDHzpynXzYfoiE9Ekx7YUFIwToBjuOwJtj8V2FXHIBlQYMaVSkiLIgHO0+9rzuvHfVKCXWiWMzh/WrfB9zUMWtPL2CQZvNbj85Joitb16WakWE064l+dkGpuvlKMqZGRczu79uCx9LoC+1dFeAbRYaKU6Ru1XfKTMNDaXchCoJQBqJq9uzZNGDAAI5E4XtXXHfddaYP/u677/LXX3/9xfdbt27NDu04FsjKyqLHH3+cvvrqK8rOzqb+/fvTO++8Q7Vq1bLvIyUlhR5++GFaunQpVa1alUfoTJgwwaFoftmyZTRmzBjatm0b1a9fn5599lm65557HNby9ttv0xtvvMFpzLZt29Kbb75JnTp1sj9vZi1C5UNbYIxaI9QMGdU/zd94kFrHF3bBjb42ydLcPogXI2HgTACkXsihetWqUvO60Za9sGBJgMgR2xHM3myvWerQuAaLJHT4YbCz6uozU1uFwNltPRLsvlrwrqoXW4Xfl7bjUSsokb4r7pgaLQs2pNDtPYuaiBoV4Hs6MmTVdwrb4/HScmu3H1tG4AhC+YqqG264gcVGzZo1+Xtn4B9mfn6+6YPHxcXRq6++Sk2bNuV/6J9++imPwdm4cSMLrNGjR9PcuXNp1qxZXBT/6KOP0k033USrV6/m1+NYgwYNotq1a9Nvv/3GHYh33303i79XXnmFtzlw4ABv89BDD3HKcvHixfTPf/6T6tSpw8IIfP311yy63nvvPfbfmjp1Kj+HmYZ4z8DdWoTKiSowVoXnmTl59pSf3W4gBK39fuwZtXDTIY5QQVCZudhi/EtM1RCKrRpqWgCM/Gg1PXVTez4+hBGiHkjB4QKt72x79fuNFF+jqsP4mYTakfbIj/JFQnrv/1buYYEy+actdPB0BqcuzRSSjxqUSC9M/ZimvvwUZZw77fxk+gdSeJUIyr6QZvL3iB/5BYXSjg9r0kNnz9KFrByy5WY5bPF7TCztGzOaHn/8CYcIkFEBvssoXqPqlryhius7VVpu7UBG4AhC6eNn01dMljOxsbEcMbrllluoRo0aNHPmTP4e7Ny5k1q2bElr1qyhLl260Pz58+naa6+lI0eO2CNGEEbjxo2jkydPUnBwMH8PMbR161b7MW677TbuWFywYAHfh5C6/PLL6a233uL7cIxHROuxxx6jp556ilJTU92uxQxpaWksyLC/yMhIj587oWzQ/6WPLj+ky+Jiq1DtmHDacegsta4fy5EYzLFDZAeF27jAQoi0iouh+95e5jbqBIdwdA8iwrXveBo1qF6VZ9m5EgAoKEd0Cx1riDbdMnEhpyV/euoaCgrw5zW5Gj+DdKP2tQD3h3RrwoXnJ9Iyadj0pabfx4ntv9GWb17lf1PlzdixY+nxZ16wW15UiwyhGzs1pvCQQE5jGllhqPMCgVNozmqutwfF6BgkbcY01d5daHLfxUGlItFQ4dSegqOKErUShJJcvy3VVOXm5tI111zDwgXRJU+Cv0wRBTp//jx17dqV1q9fz8fr27evfZsWLVpQfHy8Xcjgtk2bNg4pOESYkA5Eqg+GpNhGuw+1zahRo/j7nJwcPhZsIhT+/v78GrwWmFmLEUgT4kv7oQi+i6u/9Dsl1KS9R1MpN6/AnnZDMTdqj7TRHCuGnOjkw8VWXdiVQaUzryftXD5VZ1TY2VePU3Cn07No28GzRaJV2vEzqhNQDSBW+4KgQm0XIm02k+/j+PbfaPNXhRFjbwB/rGVk5tL0qZMcnOt/WHuAIzWwekDHZobBeVEdgGajOd7mOyUjcAShbLAkqpBWQ+efJ0lOTmYRhZol1ET98MMP1KpVK9q0aRNHmqKjC9u6FRBQSEUC3OprmtR9d9tA4GRmZtLZs2dZ0Bltg2iU2oe7tRiB2q4XX3yxGGdF8DbMFB2jXgriQ4kodMfpbQGK08qvIkjazjklAC7k5NFvOx0FgH1kTG4+z8DDOJesnDyqFhFKdWPCHY6BC6n2eq4d96LdFwQVvKXU2t29D1tBPu2c+x55G+++PY32xF5B/oHBRZ5TtWQqQqcF5wCfQaEYKxwt5EpceZPvlIzAEYSyw3K8+a677qKPPvrIYwto3rw5CyjMDESECYXmFWXcDaJfCBWqL/h7Cd6LKy8h5UXlypsJogZ2A+rihWgOUm22ErbyYyiyAsdAndSNr/3CURakHXFfK6jsXkb+frTl7zM8QxC1XqB6VJiDnxPSlLB7wBy8a9pdqiHSRq6wL3QFatfu7n2c/XsbZaefIa/DZqOUdfOKPGwUoUOdHIQx5h/e2RN1n0Q7uBbrEJ1MK6zdclY94U2+U1ZH4KgRQJ7CnUeXIFRqS4W8vDz6+OOP6ddff6WOHTuyvYKWyZMnW9ofIkAJCQn8Pfb3xx9/0LRp02jIkCGcmkPtkzZChBE5KEwHuF23bp3D/vC8ek7dqse02yAnCo+tgIAA/jLaRrsPd2txNtIHX4J3466AF8+fNPhL32j2HYw+UaSOQmijaI6VVn5c5OFyrh8doxVySA9CEGkHKauC9XV7TvDoFxyvsNtws8soG16nH/ei9oVuOVz31drdvY/s9LPkrWSeKRpd1kboXI3hwblq1SGGf04gaFHoriwRtHiT71R5pSKlMF6ojFgWVSj4VgOVd+/e7fCcJ/6BoKAVdUgQWEg3olvv5ptv5ufQjQcLBaQLAW7/+9//0okTJ+xdeosWLWLBhBSi2mbePMe/TLGN2gdEHY6F46jORqwB99HhB8ysRfBNzHoJaf/SN7ro1okOo05Na1HDGhE09d5uhfstsNGIgYl031Ut7B5MVlr5cZGfvzGFerSo4/QCiH9zsDVQXlL9L0abQJsGsVycbiMb+fn70b/6teIxMHpzUf3cQIgzrS8S7BYQufr5z8KCZny5ex8hETHkrYTF1nY58Boi1Z1VhLK8QAOCqkPSNzD4kZ9X+E6VRyrSqkeXIFQUyrX7D+kxeFKh4Ds9PZ2761577TX65ZdfeI4g0oEQRJ988gkLJXTjAdgnANRCtWvXjurWrUuvv/461zcNHTqULRO0lgqJiYk0fPhwuu+++2jJkiU0YsQI7gjUWiog7fj++++zNxUsFb755huuqVK1Vu7WYgbp/vNOnNkCKOY/O5DTZfM2pPB9ddHFaJUV24/QOw/2pLhqVbmDLD0zl80wVTeZNsKhislxRe2dWI+mm+jkg/h5dGAiDXi5aMoKbuFwHocIQn0VbpHyUxdpXNxdrUMb3cJxPxvRm2KqhrIju6ofwr7wetSKKXsIXDDRhdgmvtCnyuh9UEE+/T79n5R25iR5E2hC+XDBZkrPsbGAwPmoHR3O7xP+YjA8LbS8cP0zAd8wiCnYV/z7lg5ch2QUkVH1SUgLl5bvlNmaqmHTl7iNjn424iqP1VSZPYeCUGm7//SoGiHYDxQHRJjgKwV/KSw2KSnJLqjAlClT+JcgokNaw00F0nZz5sxhwYOIEVKREEcvvfSSfRvMJYSAgs8U0orwxvrwww/tggog1QgLBhiPQphBqMFuQVu87m4tgu9htoBXWz+kH4T80SO9qE50uGkzTEQ4liUfts/Sc2XIqVr5lbGlNt2Igc1s55CbT8u3HaXdR1MdUiuoXUHkCoLInbnopXqaQzSkWwIt3nKIx9U0qBHBwgoiD4XqK7Ydpb3HUmlA+3heO4rk8UIMfEakB6lPDELOyS/gWYer27xnj+x6C/f/azjd1quFXYTi/eE8nj2fzZ2R8PPS1sW5G1597WUNuA7p5i6Ni4gWbUSmRin4TpmlrFORUhgvVGYsR6pQU4WOtunTp1NGRgY/hq49RG6ef/55mf/nAolUeRdmvYRQrIzRLfe86ejP1K9dHI0ZXGiACTNMFHyb/et8xEerOTLw4NUtqUVcNF9kVSs/Ums8yuWiV9W2lDN8IVbpRogwZ5En1HWpGimMw1GiydU6VFH6oA7x9NjARL7AXcjO5TUFBwVwFyGOHx4SRIuQBvUrLK7WR8HsNWgFBRxhe2/hDlrx6zza/PUELhAvb24d9hB99O50u51EYIAfiyt1QcdnhxQp0rlm/aXQLThjyU6n0URviciodBzSla5SkZ7wqfI2jy5B8OpIFcTT999/z+k2VU8Er6YXXniBTp8+zWNnBMEXMFvAq60f0voz3dY9gYUF/iJ/6bbL2THdzF/nGNcyclAiVQkN4tSTPhWnr6tCBKhjkxqmanwgovQ1UtrOQH2kRVsEDzNMVU9z7nxOEfEGUXVVEoreCwWI01qZa9tQcGAAH7dWq2509fM/0omda2nb7Hco78I5Fx9IIPmHhFFBJv5Ys5l2VI9C80huJqVnZlM++8JdMhqNjImlUSNH09NPjaXcAn97bVt89aoOQufS3D1rRd0QaK7G5HhLRKasRuB4o0eXIJQllkUV6p4w/07N5wNI2yEFePvtt4uoEnwGswW8iLog7aW/6OJC9NXqvZz+6tikOv91bqZtHWIEggzXEgiRz5fvdho5yMzO4w4+M+NgtCLKSDQZmYsi0qKOCXdxt4JpcBt2jMcx9d2PEBYQLV//to+P/Z/bLqeWcTEXnxtAa/c87FAkj2P+MK4/pV3IoWFvLjXlMK8sJJw5yQNYQ4y8Nokv1iqy9/6vux3mCyKapkSEiuLge9xanTmot2IwOtdIEXJEphwFRGmOwPFWjy5B8HpRBYuAhg0bFnkctUvopBMEX0F5CZmxN4gMC+ZuLu1FF/U47RtVp2Z1oy1HOFBQ/uvFqFXfpHoOI1JU5AAXakRUMIDYbI2PElFKNN3YuRGN9WtbRPhA3KhIC7j7ymY8rsWseHtpyGXUuVktQ8uBvknhPJ4nMT6Gvvv9gOHAZETmYMkQHOhfpO7LCPV+bunSmEWd9jxpneQBvLeQBp08x3XnGZ7A+zmJIvKLacybuzQy/TMBMZVyMp3HCGnNUr09IoOfVSViSkPMWPl3VdoeXYLg9aIKNgP/+c9/aMaMGXYPJhRuw9pAWRAIgi9QnALefcfS+T4uGEjX4QKuanGs/HUOM04UkndoXIP3o0bHKK+rl7/dwBEnGE9aGWujIk/qQh4eHMg1YEWFTxxH3yCyUPMzpHsT0wXad13RlC5LcJ2ORJQNEQhtXY02VVk3Npxa1IuxnCqCqzvql3CecC3W1oQZNRK4E4dY3xeaSCHqgVAnZ9byAik9rVmqMypTRMabPLoEwetF1caNG9mvCV10bdsW1iRs3ryZzTH79OlDN910k31b1F4JgjdzqZbGtZcQtjt+LpPenJfM8/xwwUDUSgkR+D+ZNfXEX+dhwYE0++nCIcfooluzqzB6NOXebg51UFbH2qjIk3a8jH7osVb44D1AXOTlF5h23caFEt1uZkQL5iCq96J/DucU67MiRlHM/79F2/n1a/ecKCJmEKnTjwZyVduGi7/2PSc1qEbns3JZ+Ln6mUC3I84dnoO9hCsqY0TG7L8rMQAVqLKLKjiK69uki2upIAjljZkCXlwgEBnBhbpwTMlBviDgWqCEiBVTT+zzVFoWG3vqo0c5eflULSLEXq+E41qt8QG4TGF/GC/jbKyOEjcLNqZw557ViJErVHG2UU2Xeg7vC0OeVeTPbLpNCRq8Rk/XZrXp2LnzNOuJfkXSnaqWS50DfJ4qsqfO95Wt63JaNze/wHB4tfqZOHz6PCWnnOa1w3NMIjLlVxgvCD4tqpD2E4SKhLsCXvzyx4VWiQ7U76COBsOJ1WO4YOPC4S7CASf25L9P09jPfncaPRrSrQnddUUzjrggFQjPJ7OiQxVM41joLHzlu41uozXYNq+Y4s1dcTaK38deXzgHUStyNuw/yceFaSowm27DF1J0qEfTRqmUyz2OUSUkyDDdqe+yVJE9mLkqu4r/W7XX/jr4cdWODqN/dGtCQYH+vHat5QUsKCBGJSJTvoXxguBNlMj8UxAqEs4KeHER0KapcFFG4TqMN7VCRBVLG5l6QhjVig5nQTXui7VOo0fXtI/jjjlVr4Q5gvC/Mis6Xv1+I9dIaceuaNF37MGDCh1/e46cM11cjLW46nbj8+fvRx0aVedjGNV0Ya24wE6evZk9wMyI0dy8fAoKDKDtB8/SFF3KDc+bHS2jugTxuSDteZWJ10HE6T2/VJ0Ufj4kIlN+hfGC4E2IqBIEHUVnuBGnqbQ1UxALiE5pH4PYwoUXNVEQLqr4HBGOAH9iewR9hEoveBLjqzkUWZuNgEFEofAcI1NwIce8Pm1HnKshwYi2tKofy9uZEW/uImAAx0JXpDs/KwgqV2JUCROInz1HU1lw4gvnXZ0Lq8Xp+Hx2HznHQhc1bZNmbzH9OiVStXVSEpERBEEhokoQLqIuhNoZbs3qRFHvNnU51We7OAcwJ6+AU1CpGdkchdELEVx4tXVEylF7/oYUB3Ghjxrl5hXQ+excOnS6cFKBlQgY1l4rKpzSLmTz2jbsP+VgJmomkoM01mgT4g1u6xAlziiOyHEmRuF6fiL1At3z1jIWLIjc+V98nYoMXdakhmnjVWU7seXvqnzeUNtm1a7CWZ2URGQEQSjXgcqVDRlT470oA8gpPyfzRRY+UqMHtaE+beNYaB07e4GFgH4kCwQQLs7uhiMjUqSGMuujRlrnchUJMnJZRycdLuwwA8XsPzy3dOsRe42PAqab8TWq8lgdFclxN0IH2/zr6pYXRUvhQGXtLD81kxBpP1dmpGqsD6wm7n3LuqGn0RgT1GWt2X3MXkSujQLhe6Adi+LMlBTn6fKEGty5CH8snFqk9VAvZbSttrBdjaS5deJCj450EQTBNyiTgcpZWVkUGhpakl0IgleAC+TEi2kgXJSfvKEdFylDaOFxV35MGFWDyAmKviGs9FEkCKSuzWvb66+KU/+jj4ChSBp+TUZi5MuVexzqsFzZDOgFHsSJVjj+o3sCdyRimDIMLrEGFHa7Skd2b1Gbvl2zv1jeWs7qsiDStEXkStT6XaxXwuOuUpyqWH37wTM87y8zJ59vB3SId7qtVtiiqB1C9rMRV0mdlCAInhNVGO8Ao8/33nuPjh8/Trt376bGjRvTc889x07r999/v9VdCkK5oiwTxlyXxAOFYRdwJiOLh7y6S2GhVgojUUBkeDDd2rWxwxBiBaIfqANC5MdqaszIWNJVB56qw4LgO592hv5145W0f/8+p+9//rNkiYXjXT8f7ux5/0AKiYihLg9MopDIWENvLct1WYMLO+sghsyIVdWJByHVsGYETf5piylhq4rSYfYJRFAJguARUfXyyy/Tp59+ygOVH3jgAfvjiYmJNHXqVBFVgs+g0kgnL9ZQdWhcnVNsuCjDzBP1Uu7qbRBBsRXYaKLBSBTVKYeL84Z9J6lubBV6oG9LOnrWWv2P3udJFUkj6qIwSnnF1avLf/h4DQV5lJ16kpZPvJsCQ8Ppqme+cSoOrdZlXXdZA4qqEmJ6+4Q6Uaa3nbs+pUhRuqvGBkkLCkLlxd/qCz777DP63//+R3feeScFBFwyAIS7+s6dOz29PkEo1RoqXFiHTV9Cf+47yQJp6pxkvqBCnKDWyVUKS134p1x8jTObBNRbYU7e/uNp1CIuuoj4cpUaQ/2SHlUkfXnTmjzG5uPhV3K6T1kXoHYrLs7LBJWOvKwLtOSVfzgdRgyBaFZ8nkjNpIjwYEvbo9jezLbY54NXtyxMNeqEkopEorEBadPp87byLUQ6kHJVQah8WI5UHT58mBISEgzTgrm5uZ5alyCUWQ0V0NcdmRkPg9eY7TqDg3jDGhEc/Sju2Blt4TvsE2CciXoupLG0Ka+cjHOUduYUeTsQVoF552nOn387PG61Lgsmo+iKNCtWsT0GM5sVtti3vobKLspd1NshNYm/WiVqJQiVB8uiqlWrVrRy5Upq0KCBw+PffvsttW/f3pNrE4RSARfDzNx8HoKMOiqkn9AN9s1v++wXRzzmzmEc0SyjYcdGqbjUC9ncUYhBylacy1GbhaJ0fK868JCqhKXB8AGJhmmsdTOeIV+hU6dOtHXHbvoBheLplwr8UURuRXxaHcyMujcr+9YLI70od5U+dHhOUoWCUKGxLKrGjx9Pw4YN44gVolMYmrxr1y5OC86ZM6d0VikIHkJdyNLO5zhYJNx1RVOHi7IqLHflMK6NZkFIoa6nR8s6XOiOCJK2o+zmLo14+DIc1c0OXoa4gBBDl58akaI68NDiD0PM9Kxcrv+CQFQCrt6kNPIVDh87zucL0SB8LvAAW7njKHVtXsuS+ESHnlWxanZb/MxofacgjJDyMxuhRHE7Pmu9B5q2i1G6CQWhkoqq66+/nn7++Wd66aWXqEqVKiyyOnTowI9dffXVpbNKQfAArlI2MJzUXpTNDEiGgMHwYzU77nxWLrf1G3WfYf+4mKJ2y+zgZdgxjPhodZHuPzXnDxfh2KqhDmNgsB4UqO9IdW7O6U34h0TQtLnJHNH5becxPj8QMlyMb2FsDuqkrIhV5ZTvbltVoK4Fr1WDtM2mJoGkCgWh4lMsn6qePXvSokWLPL8aQSjlCJVRygZRpvTMHLqtRwJ7TSkDyLW7j3MLvjM/ppRT6XRT58ZsHomLI16LW33kCPtSUS13Y2da1oumB65uyWuCAPzvHZ2KmFHefWUzqnXRQwvvKyIsmNOCECQt68XT0mXLqXbNGuQLdHlgIp8DRAovS6hBXZrX4nO2NeUsdWpa0/TYnMk/b6Yxg9uaFqvAzLZ613RgJdUYHOBvyprDKFUoCILvIWNqhAoPd2nZbHT0XKZDykZrFokur6805pJq6C+iT0bjYVR9E8AFs32jarwthJWRmaTyUnI2dib1fDZd0yGeEmpHcZpQa8Jpj2xtPsjHxHxAbKNNI6ltCl3ht1Bw1WguWPdmYKsAvyolLGACOnjCAvvziACOMjE2B+fhl02HKLG++8HM2B5lC3gE0SGXQ5wveloZdf2ZTTV2bFKDPysrqUKJVglCBR9TExMTY/of+pkzl7xzBEdkTE35gQJxdJOpUSbqog2zyGlzkl16TAX6+9G2g2epQY0IjjhlZudz4Xn1yFAeyJuRlUdhwYWFz1Od7GvSsK5UPSqM7RtsBmNnqoQE8sXa2euVcSXm3k3+2fU2S5IPs2Hlstfv8lphpXyqFMohfsDL8xxE7/T7u7PQVON89IOWMTgaQ6pRU2U0/kdtj3ODiJb6dYffZ0owKYGq37ezOidVU6X9LI3AK+c8M4Cd4LU/d85AnRxENiJbgiBU4DE1MPUUBF9EXQARAdCmbKyaS2L7N+cls5cVXgdR9fXqffahyxh34mpf//t1h8PoGO3YGTWbD+7eZtaScirDfjHXdxpm5+ZzlAXz9OjJL1hUrXp/DOWlnqByx8BRXWFkAgqX+yohQbR2zwlKu5BjH7SM9xgS6M+F+trB0bh1NpgZQ5kxaForWJRgqhkZysOatR152uf14PFaUWGm0ocQela7GAVB8F1MiSp0+wmCL6KKivWF6K7m4RmlZapHhPA4Glzo9YXora9vy91+rvalHR2jTzlZWYtyWd87J9XpnDt0lI25ri0lxsdyqvGqxz8usi+jyJB2yDHMMWvHhLOocTU8GfRrG8diD8XiOP6uw+copmoIbT90lovo73t7mduIjt4EFGKx0HQzlGcwZuXm8/uMyQ/h4nyI5QC/wuicvjhdOyNR7R8z+/xdiCTV3aft8nP3c2UmfWglVWjUaSgIgm9R4oHKOTk5Do9JWkvwJlRRsd4iwZnHlCuzyLTMXE4LJTWIpSVbD9sjJGb3BYHTuHZk4fDlnk1ZqCDl1KNFbZplcQBxcYcyK5yNh4EYQN0WZh9egL/W+RxTYm/Ylc353GA9KNLH+bDS6Yhjwi5Cm8LT15ZBLCpBBSEEAYLXFbfgvKQ/Vzjn+Czv6tXMafqQLHQxGnUaCoJQwUXV+fPnady4cfTNN9/Q6dOnizyfn2/O/0UQygIVKcD8Nu0F3oxjukqtwd0bXkph+TbalnKGrmpTzy5WIAKqmtgXgAibtz7l4tBmf3vKCVh1WS/JUGajyJDWrX1rymkuhkd6DfVAZsQe0qH5NhsLMBiqmul01EZ09h5NpYTakTRiUCI7z7sboowkHqfrCsxFjErDB8ps+tBsqtDTwk8QBB8QVU8++SQtXbqU3n33XRo6dCi9/fbbbAT6/vvv06uvvlo6qxSEYoILHAQILmx5BQV8gR3QoT5fjJ2lZfQFzyhw13bZBQb480Vw3oaDNLBDfcvGk8oGYcrFgvNZT/Sz9PrcvAI6nZ5V7KHMKjK06cCpIm7tEFTN68XwGJxGNSNMiz0Mi1ZjZbRu9EadjvoOSnTjNaoVwWITQsSK/QDMUREQchcxKs2OOnfpQ7OpQjEAFYRKKKpg8gn39CuvvJLuvfde9qzCLECMrfnyyy950LIgeAuqqBgCSUVgqkeG0d8n09kE0igtYya1hovgMze3o5pR4Ry9Mms8ObBDfBHfIjPu7Y6mn+R22LM+XaiPRh0+fZ7d369pXygKL2TncWQKhfhwNc/LK2BBalbsacfKaN+Pq+JxRMowHgj1VxAjEBXFsR8obsF5WWE2VVje6xQEoRxEFSwTGjdubK+fUhYKPXr0oIcfftgDSxIEz4ILbFZOPkdglFBqfrHjTp+WsdoVeDI1kz74dQdNva872ybEX7RdUKafWtNOCJqYKiFFhjCbcW9Xr+fIjs1mOV2Idn3t7EBEkBCtQwE3xAksJ3764y97RA7HwkX+5i6N2b8r72L9mBGQAnheCTCj96MvHlcF7jgOuvJwQs04lauUbGRYEN/Ha9X8vOIUnJcV3i78BEEoJ1EFQXXgwAGKj4+nFi1acG0VhqIighUdHe2hZQmC50AUIDwk0EEoOav3sdoVCCd2RJ8AfKj0pp/4wnHgp/TowDYcBZrx3UL6ZfyN9n0tJKLp97t/H9juCYvvHa8ZefH76GrV6fp/f0BHM4LsogZREngofbF8t9P6pVfv6kxPfPa702NAGMHLC7d4ndlaKkTMECFTHl+unMr1KVkU9vvi/DxvFn6CIJSR+aeWKVOmUEBAAI0YMYJ+/fVXGjx4MP8yy83NpcmTJ9PIkepXuKBHzD/LB0QETpzLpHveWupwcTcyi7yla+MiJqHOQPTntu4JvE9XBqJIFeJYEBf92xcKsPI23nz16994XdsPnnUpmJRdAsTS5zrhpXUpz80voLDgS8LVlRGnSnmpdKwCUSd0+xmdezNGrVzArkkHCoIglPX127Ko0vPXX3/Rhg0buK4qKSmpJLuq8IioKqchygU2munC1Vo5m1/Rqg7X+Eyft5XmbUhxu28UeaO4epLBPEEjYeJNF3v8HB45fpJunbjIbWrv08d6swhCvROEoV4c5eTl04ptR6lqWBCPZZk+N9kenXJwjQ8NtNsMfLN6H/2jexMHEeTMqVyZo5r1yxIEQfBqR3VXNGzYkL8EobRRA4S19SjuhApegw4xVzVIqt5nzvq/afI9XS138ul9q/RAYLSrlkveBH4xzPh5nUtBpa0hu7VrE669urVrYwoOCqCc3HwKCPDn6NKb87bye/S/GJ3Sd/odT71AGVm5FBEWRLkYJ+NHPLwaaD8/Z07lxTFqVT5W3hK5Ks7PrjcfRxAEY0wPmVqzZg3NmTPH4TF0ATZq1Ihq1qxJDz74IGVnmyueFQTLA5GJOIqB9BAiSbg9mZbFjzsLtrKg8vOj7LwCU0IJtUAoMEfKyt1lSHXi7Th0jtNSEBPOwOqu7tmZvI3nhv/DdLF7UKA/n8vcfBu/d9xHik91tcGFHUIK53D+xhSKrhLMBdmI5KH78Ex64WeFWYlKxxld7PFZIy2JqJN6FuaqZrsdYdSKujWzPyPe+rPrrccRBME1piNVL730EtsoXHvttXw/OTmZ7r//frrnnnuoZcuW9MYbb1DdunXphRdeMLtLQTCXvrPZaOLsLW7NIPUXaXWhOXw6gy/S7iwLUGyN9F9keLAlF/AGNaoammxqsdkKyNvITD9nOiKHyBQE1S0TF3ItGSJXGKM3+JX59NmI3lQrKpTr0WCSCmG6emdh5+PuI+fsdVeIai3ecpiSU87w+dIXluPzggjYfOBUoev8FU05+mTGqFUrABFJU+lbdz8j3vqz643HEQTBg5GqTZs2UZ8+fez3v/rqK+rcuTN98MEHNGbMGJo+fTp3AlphwoQJdPnll1NERARHu2644QbatWtXkVE4w4cPp2rVqlHVqlXp5ptvpuPHLzlBg5SUFBo0aBCFh4fzfsaOHUt5eY4jOJYtW0YdOnSgkJAQrv/65JNPiqwHRqZIZYaGhvJ7W7duneW1CJ4F0RGYZELg2JykpqbOSTa8WBSmQIjiYqtQrehwTg2Nvb4tfftEP5r/7EC+xf3+7eL49sdx13AEJvnv09y5po2WOI4TKexcQ4cbRBQu/EfPXuA0lTP8/Ez/UyszateqyQXgiDi5i8jhwq0c2FXkCiIU1hTw6sKcPxSqQxShA3LHobPUqWkNrseCQMIxlmw5zFYOOF+YlaiNnihhgHMKX6vJP2/mVCCEGh63kpLVjt9x9zPirT+73ngcQRDcY/o3/dmzZ6lWrVr2+8uXL6cBAwbY70McHTx40Ozu7PuASPn9999p0aJF3EHYr18/HoWjGD16NNs1zJo1i7c/cuQI3XTTTQ5jcSCoMIPwt99+o08//ZQF0/jx4+3bwAIC2/Tu3ZvF4ahRo+if//wn/fLLL/Ztvv76axaHzz//PBfet23blvr3708nTpwwvRbBs1gxg8QwX32KAxfq3Lx8Cgz05/QTIh8wt8RFH3VAP/1xgLo2q0VjBrelDo2qs1DAxWncF2tpafJhFgOfPNqb7rqiKRelI0KDNBcex/PKLVyZbKIY2whcypasdhTo3sDQFz7gkTuuUpcqIhceEsSRJ23kCkOOVb3TE5/+zmIG5/sf3Zpw2g/pQBh7rt1zgkZ+/BtN/HkL152pC73201K1PyoildSgGgvVG1/7hZZuPcJmomZTstrxO+5+Rrz1Z9fbjiMIgofTfxBUECf169dnAQPh8eKLL9qfT09Pp6CgQv8bsyxYsMDhPsQQIk3r16+nK664gotpP/roI5o5cyZdddVVvM2MGTM43Qgh1qVLF1q4cCFt376d7R2wxnbt2tF//vMfnk+IVGRwcDC99957XPs1adIk3gdev2rVKraHgHACsIN44IEH2CUe4DVz586ljz/+mJ566ilTa9GDGjNtnRm6BwTzmDGD1NbS4CJOmiJdXKQhBhB5ahF3yfhT7Q9RGqSK8Hi7RtWoVVyM/XlES/q2jePBwnoXcKT89Gk+ZbLpTJhc2dW7utJgq7A2JZumzU1mkYjZiDt18wH1aTu8ZzX4F+cIYko7TDosJIA+XHzAVOckzpe/rusvPDjQHpHS7teqOWrt6DDq1qIWrdl1yXxV+zNSFv5QxfnZLc66yuo4giB4WFQNHDiQxcVrr71GP/74I6faMKJGsWXLFmrSpAmVBAgXEBsby7cQV4he9e3b174NDEdhPIrCeQgZ3LZp08YhigahBHf3bdu2Ufv27Xkb7T7UNohYAYhEHOvpp5+2P+/v78+vwWvNrsUovakVnoI19GaQyk0bF1y9a7m6SC/YeJAa1IygxjUjuL4Ho1faNKhWpB1f75z+r36t7BdxBfa/fv8pGv1J4c+AlbSTfq4bzD/7vTSHFo4vrEksT4LDqtLc33fYzyPO0cR7utKvmw/RnqOpDnYJmMuXmZNP8zceLDL4NzIs2KHeSTvzz2znJC7wShhAEKnxNtr9mjUThQDEYOa5G1J4HUjTKvNVRBW1Qq60cWVkqqck6yqr4wiC4GFRhegPUl29evXieiKk2RAFUiCig9RdccEvb4ic7t27U2JiIj927NgxPobeqR0CCs+pbbSCSj2vnnO1DSJHmZmZnNpEGtFom507d5peix6INKQUFTgeIn2COXCxxcVRbyRp5Fq+71gqd30N6BDv4Lh9bcd49k/Sp0f0bfpGxdBWZvJBhAT4E6cK9QOD4eO08cAp3hbCKu3QX/T7/x6lssY/LIq++GkJ3X51hyLnEa7kOHf90ZJfYOPZiLCJwPBmPDfl3m5s1tmiXgz/W0UNFYw7Md5GCSmr50td4JUw+HPfSf4sMe5HP/Da1WBmzHBEzZwST8raQhVpQ4yBE2mZdiFXVj+7VgWmtx5HKD28zQbD29ZTYUVV9erVacWKFRxNgqiCq7oW1Bnh8eKC2qqtW7dyWq6igKJ4fAnFA/+YIaRQsGxmwDH+3Wtn+0E4NagRQf+3am+Ri7w2veQsymIl7YToDWqHYCOAX0DaVOF1lzegqxLr0RfLC8VGZFxDFld68Gvru7H9OMI21Y1DO96r9jiq0w41X1sx4DmpHteNaVNxypXc1XlElxjEzfNf/2n4HMb05ZONBdWh0xksGvEctrF6vhT4xV0tIoRa1rs07kc/8NrVYGaIWZx7PO9qTiNSuWUVqcFn42xgNxl8psW9aKl/I1aFrFD+KMGC1Df+ICjvsUveth5fxbL5JxxFjVApu+Lw6KOPsgcWRFtcXJz98dq1a3Nq7ty5cw4RInTc4Tm1jb5LT3XkabfRd+nhPlxRw8LCWCDiy2gb7T7crUXwLPgHjAG0EAj4Hik61D5phxTrL5yHz2TQmMFJ9qgWolxG6RF9ZEobZVGCDMILEZNRmrRTQUE+bV/xGR1e8p3pmXxvunmf3R55j6rWjqO7r2xGVUKDTA9zHvHRaofarn4amwcUzWtFotVB0XBCV/vWPzdzxR5q27Aanyf8ElbCCtuYSdPhfGp/QWMfN3VuzI0C7gZe6wcza8foOEsPz/nzbx5iDXFeVhcFvL/amvPiDJyP2tHh9qHSVnFmmOpOyArli7fZYHjbenyZEjuqlwR8iI899hj98MMPbHmAYnItHTt25OL3xYsXs30BgOUCLBS6du3K93H73//+l7v0UOQO0EkIwdSqVSv7NvPmzXPYN7ZR+0BaD8fCcWDrAJDiwH0IPrNrEUr/rybtkGJtqgcX8Dt6NqUnrmtLNaPD7RfnOc8MMEyP6CNTKsryxt1duAZLpcfOnc+mAe3j+cIdk7qd7r/7Do+/19/eeYglx7z8PL74m3UOh4CAwNDWFCmbB30qzqorudq30XO390zgzjxEAPG5oDNSCU8UvjtL06k6Lf1fvIUNBeYGXttfo3nPizYd5DSvq/Tw3mOp7AhfVqkv/OxiWLTVodLF/beCC54VISuUL/j5gIAx8wdOZVyPL1Pi2X8l4ZFHHuFuup9++omaN2/uEA1DBAmg4ByCCJ2BEEoQYQD2CQC1UOj4g/Ho66+/zvVNQ4cOZcuEV155hbdB1yLqtJBivO+++2jJkiU8EBrdfar7D5YKw4YNo/fff586depEU6dOZd8t1FSpWit3a3GHzP6z9lcT7A2cpcBwsYKtgTblA8sDXMjTLhSOREGUCvvKzs2nZ75cRzsuRrcAfKnQ7Xff28vs+//okV5UJzqcphik3gKPbqZ57/67VN83LnifLt1pepjzzV0a03e/77fXbhWesy30y+ZDReblwZMLYsPsvuGCDqNPZ8eF1YE6P9qaN7T3QwzBRPWyJjU4/ao+B7w/o/STs5l/roYyq/eMBoDDZ85T70TXw5YhKjBGp6zTX+g8VSLdaP36odLFRQkmdf7dCVmh/HD2864Hn9ZnI67iPwZK87PztvVQZZ/9VxLeffddvoVTuxZYFcCpHcD2AJ14iA7BngAi6J133rFvi7QdUocQPIgYValShcURHOAViIBBQMFnatq0aZxi/PDDD+2CCgwZMoROnjzJ/lYQZhBqsHzQFq+7W4tQ9n81KRdzXIDhNRXg78+F4f+36m+HmoCp93V3iG6pyBQiL7Wiw6hrs9qcMjJKj9kK8ktdUPFxbDbavnMbYqemOrlCgwJY/KCmKiTIn6LCg1ls8jna7JiKs+pK7sweAs+h5kv7y1db7/Svq1tyXZnd1uLiNvhcrNoCGNVRVQ0N5MfhXYXuw5STGVxgbzatWZbgfbWqH8PRQggbvTXHkuTDdFuPJh4RPOr1SJdrz78SkZXxIuiteJsNhretx9cp9/SfO+BuDqdzfDmjQYMGRdJ7eiDcNm7c6HIbpPpUuq+4axE881eTmTQV0n0qTYWIRrO60W6L2QEu1LAOQKE1DEGRyjp27jxVCSnaJQjO/g2hUza8MXwI9Xn+B7fbIQqRkZXH0STUFSHFUzge5iiLB5wbGJLuOXKO78M2obiu5Gafg7h9/NM19mJ51F2Z+avWnS2Ato4KqcZHBybSxNmbaez1RFe0rmMprVmWf2XjfeHnED9jiCAhUljaESTsS13w5MLnnXibDYa3rcfXKVdRJQgl+asJogGRGqtF2Ih0YF4fCoSVCJt1MT1mdNxqQWU3KDw/P5ejOzaTzuFqZM72g2fY4BSROESsOjWtSbd1L4xYAETvTHejOXEld+VYbvS5wLEef9UiRuWqRduqLYASdYg29k6sS99bGLZc1n9lSwRJ8HYbDG9bj6/jfQPJhEqN1b+akE5BtArRJjPRCsycQ9SqVf1Yti1Q41KM0mNIKcKG4KnbHdPTpQmaIbA+Vyjn8I5NqrNIPHHuAm36+xSLDUTjOjapQZNmb+H0Uj46en7azDVlqhvN3b6xHSJNzo6rxtW4+1xUOhaRx5kr99L0eVv5FvMBtZFqZQtgZgxNoS1AoR9Yk1qRPvNXNkeQ/P0dboXKifWfd79KtR5fRyJVgldRnKhF9xa16ds1+11GK1S7PaI4gQF+lJtfwPYMB09ncKeZ1sRSgZQifJ2m/HSSyoqt27ZR48ZN3HZyIfK0ft8pTisN6d6EI0PbUs7yLz2IKLyvJ65v6xC9M9tNh/Z+HKN1/cLIF2wl9N2F7j4XmIa6S8eqFm08Z8UWAJ5UGLSMmjL5K1vwNbzNBsPb1uPriKgSvAprZoZxbPyIi6uzaIW+g0w5rWvb7Q+fzuC5c9ifOq42pbh421Fqe9sztPmrwm7S0mTOjgs0sgnZ66IgSPQdY3o7CUTfsH1CnUj7cF0IKn2tkVlXcnTUjR7cloctN64dSRGhQfzc+axcu2WCq8/lmnbxXMxuNh3rd7FLzoz9ANaXnHKGLmtS3d5RKOaXgq/hbTYY3rYeX6ZcLRUqG2Kp4B5Vb+Lsgqw3fczLy+d/5DNX7WUncL35Y1pmDv8V5sqhfPS1beivk+nUpHaU/bhGlgvHt/9WqsJKuayr93YyNZOiL47p0Tqn6yNFqtW5ekQIfblyL9smuLJQgLEnzhMMQnGecnLz2W7giU9/tw9NRrH5mfQsah0fS1k5ebRh/ynq1qI2ixd0SxrNX0TED2uH+SrE3T1vLnUrdGaO7kMRYcGUlZPPnX1sgZFXQMu3HrHPIVSCD15TaCYwsiIw+/MiCN6Ct9lgeNt6vG28jk9YKgiCkT/VoTPn3f7VhKiFurDiNfBOQneX0Uy7wAB/SmoQy7PsVHTHKGKydvdxe7REP8YG1GrVja5+4acijuolRTmq6zsbMbrlq1WFIslsETbeN0QYBIqz6J3elVx11Gkd1FWx+bGzF/hzad+4OqcBVbekkcEmPo/m9WI4tar/3JxFEGOrhtp/iRvNIUQaEZ8H1lArKpx2HDpLmdl51DIuxv5L1I/8TP2VrS0QF4TyxtuaGLxtPb46XkdEleAVf03gcdvF+3Vjwikrt4CFzkP9W3ENTHBQAEdU8JrwkCD+x4NU1IePXElVQgLs412c1e+oobr6+XBaEZOelcuGojgu1mIkSvz8A6j1lffyl16UDHh5nqHpJArG2zWqziIJhdpmRBJEjRJJVoqw8csEYgXisSQWCqrYHB18EFcrtx/ldODkn1yPscC0laAAf7frVvVq7mqu8D5gcJp6Ppuu6RDPXY0QYTiP2l+esErA54YC9oWbL6VMsR90eV7IzuWfGxFWgrfhbTYY3rYeXxuvI6JKKDMBpTrBjP6ayMzJp+/X7qf2japTi7rRHHFKqBNF6Zm5LHrU9kjj4OLI+yeizX+domvauzd/3LxhJX3xUqEDvhF1xxfvPba/7RWK7dW0iCgxMp10JtTciSQzQEDk5dtoUMd4jv5sw1BlzZgaZ3AkJymOU3hamtWNYm8rRABRs4b5izsPneMCeJtbywrXnlhWLTB4SDUVCm93Iqx6ZCgXsaOmS6VMX/luIw+bVtvYvPivbkEQfHu8jogqodTDsR0aV+c6HneRpITaUdS6fiynmRrWjDAVeapfvaq9ONsZC8dfW2rvdeNXz9DQr57h9+nKdBL1TqgVMiuSIGpwHhGtQspNX7tkVISdlZtHMVVCOD33wa872EXeTEcP6pX+OpFepLAfcwi1ghb7wugbfaG8/r3GVC2sg3Im6KzMIUQEscBWQHVjqvD5G3NdEg/W1p4LHANiD6LdVW2VzC8ThMppFD20DI1/RVQJpRqObV4vmn+gzUYlIAxCgwI5zWRme8z1++a3fU6jMaUpqLRAhIz4aFWRQnlc/DfsP8lRIwgWd51q7kSN0TBp1eqMyBLOP8475hyasVDAsfB5dW5Wi4UvBhMjzVecVKqq7bqxU2O7J5bRZ2hUr2YEiufPZ+ey2EaHI9J6RTo3z5zncTWoIdPbZJTmL9jSLooVBME3x+uIqBJKNRwLkQFxYOavCdTDRIYXzr0zKiw3imLUjg5zmlI7tvlPKktuapJHTeNiihRxo1sNvwAOnz7PkTUlNpR3llG3ohlRM/nnLUWsBtB5p86HKwsFZc+AdR07l0lR4UF01xUJ1KlpLdMCWM1d1Kctw0MCOO3mTNCZnUMIwdewhuuIJWrWEGX7eEOKS+HpqV+w3lYUKwiVFX8vNf4VUSWUaji2Z8s6LiNJ+gs2iqJxEXVVWA4QAUPxcYEt1GlKbct3L1BZgvmS/V+aY1zvc20bqhsbbvdjuqZ9HHcuajvpmtWJsjQcuEPjGlxDpAQE0qYw6FTnw2ggsXag76a/TlGPFnW4KP6WLo15f+5SqUZzF4vUdhXY2K4B/l9Ggg7rdJcGtVp3te9YGs07cs5UY0Jxf8F6Y1GsIFRWCrzU+FdElVBq4VhcGIMD3XeC6TvOtBdLjEtJalitSDSnXmwVFlVwWTNj/lhWuCvi/mjxLnri+iS2BNBHYFobGHa6SmEh3Tfio9UO0aK/T6YXOR96CwWgvKggrnDe0V2Jx6zOXXQ2RgaF8hC+sKlIy8y1Czr2xPJz/5lZrbtSAs9MNK24v2C9sShWECor/l5q/Cuz/4RSC8fiQlfc1n7YEMDBe8x1hSaciOa8OW8r36JlX/Hr5kP2EQveDC7+8Hz6983tKa5aVZry86W5gwoIR0R0zNYIwDRTLxjW7ztp6nxo5/jhvCu7CqsCWIlnmKX+MK4/1yphgc3qRbE/GCwQ5q5PoVsmLqRrX5lP+4+nkb+/v9s1WjkXEHiIjOnPN6KA+Bn0xC9YRKjMRvFOpGba5xoKglD643VcoWpOyyp6LKJKKLVwbNdmte1RCTPDOlHng8gJQBpLjTqBqzm8neZtSOHbu6cv4ceDAgO4mFkVZMNuQXucpJvLNv0HewVn4BL7a/IhFjDOaszM1hopUYPibIgZiBpFTn4BF++PMjgf2vE+qg4LVgM47/jsrApgzEvEwGl0BKJrE/MXeWjyqr10NiOHt8N+sUZVI6XmATr7zNQarZ4LJfDcia3i/oK1WhSL7QVBKJvxOq5+16nxOmWFpP8Ej4dj4QKOH+SwkAB+zOywTkROJny/0XI9zegZq/nxIvU7vZrS0DKsq6rRKsntxR84cxtHlM6KqIF4gpjRd8Hl5BWwIDEzP1Cdd7BuzwnT3laI9mBeIvyrJs/ezJ15zgrJUUvWv12cw2eKJgRgVHOFfeOXYEnMS/Viq6Tzy7y1KFYQKjP+MI6++Hvkrl7NvGK8jogqwePh2M9HXEVRVYK5IHnynC3cyWd2WC7SWYhsWKmnGdihAU2cvdlekP2Pbk24lgt/xXRIOU2tNTPiSntunyvwD93VxRn2C2ZFTd82cbRyxzGaNHuz0y44UCuqqBkmhCsiVDjviAjCrHPxlsM0d0MKR53MTqtH1PC27k0oMT6Wftl8yKXwHTGwDdfAHTqd4baIHtldzALUDrg2G+E0Ep5DezUt8S9Yby2KFYTKjp+XjdcRUSV4DPWDHBsRyveNohL6vyYGto9ns0hEV/q2jaOuzWtTaJA/16/MeqKf4dBeZwXTPAx4feFFH8eGSeT6/adY8MBeoTS6AWfNnk8D+vXh8Syu1op/1te0i3d5ccZrlCWAO1EDH6hXvt/otgsOYguO6MoCAOe9U9MaXNsF00+IDNSlKfsBbDPKpAD+fPlu9o9yVhAOsI9C89JcHiUEQ1Kt3YFRET1EEKJX2N5KhNMomgYxiX2V9BestxbFCoLgXeN1RFQJHkG1m+OCk1dQwCkQRJruubIZXdepIV/YQURYEN3atTF3m+Xn2ygosFBA4UJ77nw2DWgfz+NpUC9lNLRX7z+kr6fRdo3BCBKRH6Qj33j2Pur37fP8HFJNeh8pmEz+vvM49WhVaAHhbj6fuvjf2COBTqVnO3gWGa0VF/9qF+uQnEVgdpo07ER0KScv30HEuOuCO5l6wSGCh33h8/q/VXs5gvP1mKvtohARJaM5evrUodFxDWvJLlpl3DpxoV3oQUAjkgmz0ws5ebRm1yUhqtJnql7CbITTKJpWuLGfR4tizUTxBEGonIioEjyD7dLFB/8t3nqIvh3bj6qGBtmtD5SFAFrtn7yhXRGTSxQ9NzA5nkb5D6mCaSO3bhX5efWuztTCwMJAu19cvKuEBbHIs1o7A+8pbfoKkSp0L/6jexPeDqacuPgjGrd821H2onJ2cdYaduqjekrUbD94ht+PGZsDJXqOns2kJz793S5EMDi6bcNqHMXRemUpI8sqBQWUkXXJCkGbOtQKGGfH1Z8n7EMv9H7ZfJB2H021z3RUQvRkeqY96qkGaxvVXUG8aAWeJ2qnXGFG5JXGcQVB8B1EVAklhjsr/IhsBYXCCfPZburUmK0PIFqQ/sHjSAPe3CyEHrytB02/v+h+FroZavwLEY3Vv0Z3P3K843NGxyEKoJ6jPqCw2JpFLvRWi8VxoVWWD46RqnDamnKaU0b4wsUf0RmIiJjdx51enBG9gQjDqJigAL/CYcJ+fg6iBum7+tUjLHXBwdwTtU/aSBP2jYHJ6K7UC815zw7klB46Lt1hdFxXheRK6MFyQe1fK5pz8wrskarvft9Pf+w9WaTuKu1CofDFe4LgKoviVG8sihUEwbsQUSWUGFxI4D2EonQIlEcHtKbQ4EAWUkjBqXTcL+OvpfnkDeTTyqn3kV9AIF39/I8OF3q8FysdcDsOnaMnPl3jNKqGKF3y32c4soZ6MYiul2atpw8eusJldx4EWYt6MVxArnUFx3Gfvqm908JsZ11wqC2Ckao20qQeNzIstSoujY7rrJDcKLplZJ4JvxcIVaO6K4BZhVqxZSuD4lRvK4oVBMG7EJ8qwSOgYFl16wUE+NnvKxNHCCpvw5afR4tevMHhQl8tIpTqxlYxbSj3v0U7nLqoT5+bTOEhQTyORtvdh2jUA++tYLdxzC68rUcCPTYwkaNGEGGn0rL4NcfPZTqktvSmnVa64DKy8rhDUpu6cyWG1Fqt+osZnSej9TqLbmnNM9113OG94D3BWHTmyj1lKmq4KNbf3+FWEARBRJVQbHDhQzt+oeHhpTSWP/nZ7+PCuXW78fw+bxFWmWdO8Pc8riXQnwcfuzKm7HexWNyoQLqIi/q5C+zXBSAslGBD8fr4r/+kkR//Rsu2HmHRg6hRzahwFqTgtR832Qvy9aadRoXZVsSWOzGkXasr1HGjwoPp2yf60fxnB/ItbDGGXdnM6XqdCTqteabquDMj7KTjThAEb0DSf4Jl1AVPtemjS0xb3A2Xc3UfF86Jj95G3sxv7z1GfZ752h7ReeC95ez+bVQgrdJze4+mFokiOet8u617gtPuPm1qS1vsDAuCyxNqUELtSFOF2e7Elt5ywNnjCrVWV/YKMPTEcUGDGlWLFLtjvRiq/Ob8rZYEnWoAkI47QRB8DRFVQrGsEzBYVnXSQXho0zRIb6n7SCMVFOSTN5OfnVVoqJlUeKF3ZkyJAmoIDLx/GGWqKJIrIBBgG6CYNjeZHcZddfdhDRmaIcTawmwMlzYqzDYrtrS2DO4ibdhf49qRhuLy6qQ4qhEVxp+1u27NxwYkOtSFuRN0WvNM6bgTBMGXEFElWAIRBAgqbeQAkSlcZFVxt5+/n92LCWmkgAB4UnmvsAoICS001IwOp4jQYBp7fWH6C9GavXNSOWqFNNTp9CwWFnAot1LEDSuFQR3iHUQQ6qnSL1oWYLQMBNrSrUeKWBZoaVE3ml4d2rnQzNK/sCNQzZhLauAotvB5wNwTEa8TaZlFjp+bl08LNh50uXYIpiohQbR2zwmqGhJoL8zG+wkM8OPvVXOCHn3hOQSqcnF3FlUzSuVJx50gCL6EiCrBNLiAn0zLKjI+JjM7nx2+VZomJzefL+jq/uYtyZTYuhV5K+Pe+oYjN9sOnqEN+085mHfiKt87sZ5DNAZRFisdgohUoRAdJpdhwYGUl19Aq3Yes0deYJB6e8+mtPmv0y4jR/E1qvLrcdzPlu/mxyCWsO8zGVlFPKW2rtzDhp/KURwjW/YfT6P1+05SxyY1aPK93YqYqWpRESXUmaGAH/v8evU+mnJvN9qWcoad8M2MEoKlxshBiRQRFmwYVXOXypOOO0EQfAURVYKltN+GfSfpievbclefuoCnXsjmiISqFYJguLJ1XXvapmXLFuStBAcH03/vH+ggLiA6Hujbkr2i4KWkH+xsZZwMBAK6+TYeOMXnAp5WsDbQptQguiC0Rl/bhj2+9KNu7GmuQYW1VkpQaVNlGMcz+pM1DseHmWrN6HCa8nNykfScSncidQb0tg0qrZabX0DRVYI5svb813/a5zLG14iwm6yarStDAT7eJ3y4EAUji6k8bxlDIQiC4AwRVYIpCqMDxG7guKhqi5IxWiY8JJCdxQtHomRejCLY+D46BI+eOU91YquQtwmqOev20YiPVrPgGTM4iXq2rMORmbwCG13IzuM6Kn00xuw4GaS54DcFewQIpP/e0YnPG0bgIB123cXhz+j6g+BAx6Q6p3g9RM/eY6kUeTHCA0F157TFRSJhfKubS9eiXjQ/pheEztJzEJN74G6uSavh/eNzxS28tbSO9Yg+WXKe9/djGwl3DQA8QkcEkyAIPoqIKsEtiBxk5uSz2Jj806UCdQUu5jNH9aGWcTH8PQw/UcQ88actlHIqgybd05Uv4ANenks3JgTTg7ddXY7vhigkNJRWrttMz/24h6bM2WKvmYL9Aeb+6TvYILb0KTLtOBlnAgGCqnm9GHv9ECJ7SojgfoEtnsWGu/E5iOpoU376SBjqo/RdctoZiK6AIET3Zp+ketS/fX1O3eJzhhAOCw5gkQNhhRQo9q3eg1VzUKQenTUAYF8pJ9P5nKn5kSKsBEHwRURUCW5RF1ZXUY//fLuBJt/TlSMYSBkp8088h/QWLsS4qH67O5se+3AVX1S7NCtMIWJ2XzhqhVbupc91wkELit8hYrJz82jfsTT6+1QGtWtY3T56BSGbqxKLpuyQBkMqD8OD/9x3ktc5c+VeXhsEVe82jjVT7uYNAr1AQPE6hkZn5xWwMEGaq3X9WC7qhnBC5EgrRKxGklAsrtBbL3y/9gB1aFzdwf5AOwPRFeo4cDe/bsIC+mzEVVQjMtRetwSRg3Sdisqp96DMQc3UlaFoHkINn58zGwlVvI5jKUd1QRAEX0NEleAWRA2Onb3kmK4F4gCiAhdxkJ+PuW2OZqD6qIZ+7AjqdFBr9IULQQVwQR7aqxlViwjhgcKYf6cd8gvxFhdbhZ9TAuPgqQwWLyisViIAggEiz6qwgYDSF5Kr93I89QLXDUFYpV7IoZ/++Es3CzCODp85b1+DlUgS3jMKveeuT+GoD/aFTkWkBkd+tJpTlRCMKrWG0TfoKLSSnoO41ReJq05PzGxU+z6fncsi6dUfNpquKyvsQsxxGdVTNW2IyuH9QthJtEoQBF9DRJXgFkQrcOHURiQgYFTaTFtjhW60JrUjHS7oiGrg4uosqmElqgK3bVyYf918yCFypKJHGw6cogY1Iunc+Wy+iKPjDoLwg1930NT7urMIgMEnxE7r+uaFDYQKhJDRDLpLNgDkNuIV6O/H31t9zxBsjw5M5DRaSKA/m48O/3CV08hZjci6ltNz2iJx3MLcVb0XtW+kCjs1rUlx1aqaqivDPhHxwizI+RtSOIqo71LU20ioz1iK0QVB8DXKdUzNihUraPDgwVS3bl3+Rf7jj4XDbRX4xT5+/HiqU6cOhYWFUd++fWnPnsIZX4ozZ87QnXfeSZGRkRQdHU33338/ZWRkOGyzZcsW6tmzJ4WGhlL9+vXp9ddfL7KWWbNmUYsWLXibNm3a0Lx58yyvpaKiIjtatGmz+95exsXXaNdPqBNFWbn5Dhd0FGkjsuJs5Im21shMVAU/KxBzGKhr5NT9265jdN87y7gAHSIGgnCHprg85VQ6R4zUXEIzwgZzAZGuNEJFeGau2ldEfOpnAWLtqJOy+p4hYjELEJYKqG8b+fFql/Pwlm07Yjhmx5kgRIRNuZgDCCGcL5tu38999QdH3CCWYEGxNPkwi9ePh1/JgguiemivppxGLEzVFvAxsO/dR1Pts/oGvDyPb/XzCNX7lZEzgiD4IuUqqs6fP09t27alt99+2/B5iJ/p06fTe++9R2vXrqUqVapQ//79KSsry74NBNW2bdto0aJFNGfOHBZqDz74oP35tLQ06tevHzVo0IDWr19Pb7zxBr3wwgv0v//9z77Nb7/9RrfffjsLso0bN9INN9zAX1u3brW0looKLrDXdoy3z3b7/sl+LCS+We0oIlRKa/XOYw7DeNEth7QOLsRGF3qrRc/Y/ujZC3w8LXdf2YwHFHdrUYvXiW67oAB/u3hBegkiALVOmGtnVdjoBwBr5wCitslM+hLDglUkyMp7xmcAKwa8d9RQIeXnCghZ5RXmCiUIsSZtus1ISCvQjfjXiXQaPbgtJcbHcgQKkcFbuzZmzyxEmVA8P3rGavp+7X5ee0Ex3q8gCIKvUa7pvwEDBvCXEbjoTJ06lZ599lm6/vrr+bHPPvuMatWqxRGt2267jXbs2EELFiygP/74gy677DLe5s0336SBAwfSxIkTOQL25ZdfUk5ODn388cfcQt+6dWvatGkTTZ482S6+pk2bRtdccw2NHTuW7//nP/9hkfbWW2+xiDKzFiOys7P5SyvwPA2cyleuXElHjx7lKBoicnAw1z53+PBhOnnyJNWoUYPq1atH3bp1o+XLl9Pnn3/Oa8J56ty5M509e5a3qV27Nr/+yJEjtGrVKhatGRcyafuOHZSXm0MB/oEUFBhAeXm5rMvz/IPIln2eFo63oQALlupEutE0AeOLrj0ipga9+sEP7M6ttwQwQjszDjVMN3ZuRP+pcjm1a1iNIzkQAkiDId2HgnQUid+hGaGjTZGNu6EdCzArF3q8HtEYfT1QTl4+/WAxfamibWYNRBGlQlRn/rP1TAlBCNntB8+4HfECQag10lS4EkEQlxjTg2YBCNseLevY03krdxxmQaeiT41qRtr3fXPXxg6pP60Xl/79SqRKEARfxGtrqg4cOEDHjh3jNJsiKiqKL/5r1qxhIYNbpPyUoALY3t/fn6NJN954I29zxRVXsKBSIML02muvsYiIiYnhbcaMGeNwfGyj0pFm1mLEhAkT6MUXX6TS4vvvv6eRI0fSoUOXaoLi4uJYJAL9cwoVKdHy7rvvmj5uXn4+sZ6yo7nIY782cyNp0s+epOG39KCnIyMpNTXVVNGzmhnXrE4khQUFUqeEmkU8nhA9GtI9gdNXfroROgAXfAxN/uDhXqaFTd82cey/pbr8UKv1287CeqCp93Wj0+nW0pffrN5Lt/VoatpAdOaKPVzQj4jfiIGJdN9VLQxFiZaNf53idCzScHf2bMrHKTIY+lgqNaoZUSiGNUDUODs3KrI4T9dwYES1yItz/Pz9KT0zlz8TfQG/1nhVhiMLguDLeK2ogogBiAZpwX31HG5r1qzp8HxgYCDFxsY6bNOoUaMi+1DPQVTh1t1x3K3FiKefftpBrCEqhJouTwmqW265pYg4QlTq5ptvdvla/WvKG5yXKhFRlHrurFszTeX5NGJQYuFgZye+WcrjCdvUig7jjjKtzxMu4K//uImmXSxedydsMIYH+x3SvQk/9vQX6+zRmOKkL2cs3c0RntHuCr0HtaGMrFy644qmnFpFl58rUaJ9/VWJcbR821EWXuge1M4MRLTv1e830r9v6WAYFYLw03tfKazYKdzYqTF/b8ayAnYKMhxZEARfxmtFVUUgJCSEvzwN0nqIQhmJI28TTGa5kJFGW/emmGq7b1kvmhJqR5m2QsBcPPhntW0YS+M+X2uvR1LO6Fp/J2cpMlgJYB8o0kYkZdp93biLEAJjx6Gzljyb8Brw3Zp99NigpMJhwVc05WibflgwBBUc1636aGmjehhkjLl7GNiMFKKin5uoECJMRulDs2N6UOPmzt9M+zkhMiaCShAEX8ZrRZWq6zl+/DjXCilwv127dvZtTpy4ZIoI8vLyuCNQvR63eI0Wdd/dNtrn3a2lLEGdlFFaz9fp2b0LpZ86xpEYV233iFgdT8007fF04HgardyxiwXCrCeupu9+P2AXL20aFNZjcYrsiqb8Gr2wQc3UuYxsqhISxMLoK120COIFmBEZ2B/mIkKEcTE2iriJWHywjYB/YV2YqpuqGhpkyUcLAkof1VPiCWLI7Jw9gAgWjsGir1cz3p86Nxhd465e67buTSx9TuJNJQiCr+O1ogopO4iZxYsX24UL0kSolXr44Yf5fteuXencuXPc1dexY0d+bMmSJVRQUMD1Tmqbf//735Sbm0tBQUH8GIrQmzdvzqk/tQ2OM2rUKPvxsQ0eN7uWsgRF6RWRrIw0jgTBlFOlqRDVmbO+sPBZXawb1Khqd0Q3Uxh+a9cmNP7rP+3iQ4kXFLKv3HGUliRn0h09E+hMelYRMQchgs5Hd67rowa3cRrxgq3Da0M78wxAiAxtXRGnFqPDWTzBe2uKJo03Y/iVFBDgb0qUQBAixRceEmSP6k2bm2zvTky7kE0JtSOpU9MadrFoJiqknq8ZGcrnSDmtq5ShkeBS+8eoG70tg6vPSbypBEHwdcpVVMFPau/evfb7KAhHZx5qouLj41nkvPzyy9S0aVMWNs899xx3qsHuALRs2ZK79h544AHu0oNwevTRR7lwHNuBO+64g4vFYZcwbtw4tklAIfeUKVPsx0UqrVevXjRp0iQaNGgQffXVV/Tnn3/abRdwYXG3lrJEGy2rSMTVqcXCSVs3pB0sHBEaxDYBEANWrBAwIkVr4glrgs1/nWYR1LV5LY5Awfxy/f5TNPqTNQ6vN+u6HuBPNOraJMOI142dGlFosOsUHtZyVVI9u9EmqBYRyrMIzYgS7BeiBFYHX6/ey6OCPnzkSvs8vQD/ALY70AoiK1EhbKsEj1746AUXj9K+eBxLQ5dFUAmC4OOUq6iCcOndu7f9virqHjZsGH3yySf05JNPspcVrA8QkerRowdbKMCgUwHLBAipPn36cNcfirThJ6Xt0lu4cCENHz6co1nVq1dnE0+tlxUsBmbOnMmWCc888wwLJ3T+JSYm2rcxs5ayArYJ6PJDUbqv1lAZMfSFD9hI1LDo/No2dCw1kw09p9zbtVieR8rEE3PuUFukIleTZm+mtg2rGdZFmR0n88smRIua8VEg2hDxwqgYiJvgwABLdUVI4+E+xKBVUVIzKpxu7dbEPqQYKbUlyYftM/VKIxLEHlcXb0+lZdkjcf+6uqXlz0kiVYIg+DLlKqquvPJKl6IAv6Rfeukl/nIGoloQRK5ISkriOiRX3HrrrfxVkrWUFfChQrQN3X96ewQjuwRfIDS8Kq1NyXYrOgCyY1Y8nrQ+mVoTTxW5SmpQzWnxtdVxMhBsb/y02T78edOB0xRfo6rpUThR4UEs5PB+UVBvddTMjCU77cX9reNjWeDA+bw0u+rU4GXMCdRG4lrXjzFdwC/eVIIgVATK1VFdKD433XQTffvtt2zmqQURrO+++46/8L0R3lYMHBkZRb2e+sqt6FAu6nn5BfZ2f6Tn4N+k3N5xi/t4XHke5eWhFNzR0kA/fkZ1Ao7Uub4Xx3Vd1TFhfy3jYorUWBmh1gJh0TuxLvVoUZsjTVpneneiBJEtpPhu6dKYzmZk0fyNKRyBg/O5dgSNp8G+p/ycXGRED4QqnOutuLoLgiD4Ml5bqC6YE1ZweHfmqK6eM3JUX7ZsGf1n8juUn5NJrRIaUo9uXen06TMUGhFNGRRO7RtVp31//U0b/1xH27dto/TzmbRjZ6GjeqB/IAVedFTHsQKDQ+hCRjo3CBRGyqDV3RuABobH0K4dW2n1gQyeHegKbeoOo2cwUBjF4YH+/g4DnbX+TSiURi1WwxoRRRzZ9UIIz8GuAWhtHRD9seq6jtejYxD769s2zlr9V1AArwU1UHCFh4mpWYNQpDERdYNIbBEXQ/WrR9DBU+mlKli0g5f1aIWqu6HLYqUgCEJFQESVjwNRgzSq1ef69O3LMxEhSCAecOFvlRRFAzrE0+TZm6lu8/rU7+q+NLHmZRTaKYMjRLhY6zvj0Imn0nJI86BIG3VP+iG5/S5ePNG1pxVQDevXoZ+TL81YdCc6UKcEIFpwhXZX/J2Tm8+dfnrvJq0QwgX9k8d680UfQiDfZqNerepw8bXVcTJwXUdHIeqaILCsGoNivThOXr6NuxPNiBJExXA+4HOFLy0Ybty8XnSp1SoZDV7WohWqziwrRFAJglBREFFVCVGF23pBMo8jJH7scQRRdOzcBfvzzsaRwBcJHkMYoPv58t10VZt6LMDU9lo39GPnMovU2FgVHUj94QLcqn6s6eJvvKafzrtJrQ3iC2lFdNqpDjaIIVg6IEqVkZnr1FncKFoEQQnbApVihCUEjm12riG2CQn0p/3Hz/G5euDd5fy8K1NUGJOu2Xm81GqVIHpU16C6VSLIXYefdt7iQ/1aGtoyiKASBKGiIKKqEoKL2baDZ+lf/VrRmOuSHIbbqshCn6R6lrygkJZTKTrU9MBlXO+GPn/jQZpybzcHgWJl5AkEAnybYJiJeX9mTSWrRYSwKFGO7ArlEQXRdSI1kw6eyqAOTWrQ9LnJDlGhxwcnuXVdV4INIvPZWzpQQIAfu6Q3qRXBNhBm5xryObXZqHX9WP6+d2I9uyiBWNX7aKGzDxGg2ReNPfX7LMkcPSV8kN7Temv1b3cpwuRq8LIWCNkNB05x1ExrzyAIglCREFFVCdBGGtg/yN+fYqqGGNYhKeFhtRZIddThe4xVQcF0Vm4+rdpxzMENXZ/OMjvyRBsJgpEm0lxmBd+Qbk1o9IxLKUlt2mxrymk25cRsP4xygbjSR4VOpmdSXkEBP44OPYg5ZyN01DpxnhGVwSDh5L8LPbHcpfBUTZqK3GB4sta1fKImWqh9nTb65qlaJWcdffrZilbTo9LhJwhCRUZEVQVGH2no0Lg6tagbbWqOXHGGBKvvMRMPQLTpC9CNisG3X2z5NztMGaLNiuALDPCnyxNqsKO4VghhJmDLejFc9D53Q4pdXAIIChUVQhowJDCAz2etqFC6pWtjXoN2hI4aD6MVMijXh4P6U1+s5fNq5D6OWYDK0BTvWyuAkIJEptaVazle17ZRdY6KebJWCT83EFRm0qtW0qOCIAgVGT+bL5oa+SgYbQMz0tTUVIqMjCzVY6lIA1rdIaCQdpl2X3endUgK5a+0bs8Jiq9ele55a6nbCMSMR3uz4SW6z9T3uOAmxsfSPW8av7553Wh78TuKz1UETY1x0QsEFQlCjQ5sE4wEmxEo1MaYGhR9q7QZrArg4QQWbT5IU+ZcGg2j6qwggpZeNM2EUFJ1REqoOFunVsig+1BbmI/3fGfPBGrXqDrbH+AzwnE3HjjNAg/nHpEtq3VNrp4rSUffsOlL3H72n424iqpHhvJrps5xTJuqbZTYLE1bB0EQBG+4fkukqoKijzSYdQZXJpQAKTGztUCoR/phXH8KDQqgyPBatC3lLNcrOXs9Ik4oZlcibsGmg3RVYl2KqRJsjxAh/fX3yXR6+dsNDuktK3VYiEqt2H600EFdkzJD9GnsZ78X6VI0cjjXCiV3s/AcIk26Im4cCzMIjRjUIZ6uaVe/WGNiXD1XGh19RjP78F7dRdREUAmCUNERUVUBMfIOsuIMrvygLmTnmqoFwvHqV69K367Z71CjBVB3g9pyjHFxldbbcySVRcWvWw6zAAIw80SBtl74WKnDguBbsf0IR6xU2g/eVbWiwovs16jIHWNejMSAXsjgHCAypRVZZou4vW1MS3Fm9lkRm4IgCBUVEVUVEKNIQ3GcwQvtBQoMPYZULRCen/LzliKF41qvKAwahomlkR2ASuvhQox1QzApnNV1WTGVhNWDmoWn6p9QW/XowEtzHd1FYVwJHXcdcjd3aUxfrdrLI2d8pYi7JGLQ01EzQRAEX0JEVQUEF2fU7GBcCyJUKpVm5UKJyMvvF+uqakWHUWRYEN3UpRGFBweyBUGhFCD7eBI9+jTawdPni9gBqK7ASzP6bFzwbSbNZ1TwrgQbxBxSj4dPZ9AD762w10sptD5SZqIwJe2Qe/WuzvTEZ7/7TBE33rN09AmCIFhHRFUFBBd7RIa041uu7Rhv2oQSF8qsnHwuIodDOIqnw0P8aVvKGbZiuO/tZfTE9W2pVVyMqRotRLnSLuTQLRMXuhUV2pSeqzSf1lTyX1dfMpVERCjQ34/W7j5OL85aX0RQcRQryXFUTXFTclY65O7u1YzNUX1hTAvWIR19giAI1hFRVUnc0vcfT+PuP7MXSkSj1D7GDE5iM1AMKZ65qtAQ1EqNFvaBNBi6A13VZR0+c94hpWcmzYcxOcpQEu991+Fz9L9FO5zWS+F4SFvO+e7SqJriRGFczbwzqs2C6IP3l68UceNcaj2yfEEMCoIglDciqnwco3b6/1u1r4hwsjrcltvjL+4DqTaMaL5KYwhqtUYLXYEfD7/SIU2nRMX5rFz698w/aPfRczRqUBsHk02kAxvXjjTsLNPXZeG1vdvUY6GF17l6fyis36kTXhCN6JJUKVNXbiPF6ZDzpSJuNoq9mF6Vjj5BEARziKjyUVwVSCPdhq415etkVIcEs0gUl+svlAUGERjs4/Wft1CXlrXtdVlWzUEv5OTxaBxtXVXhxBkbZeXlU8cm1alxrQg6kZZJp9OyqHZ0WGGRuH/hLD6kIU+lZdJt3RMoMMCPDUa1dVnu6qy0QgBnRBuFgckmBBnqiLQpU/1IFld2CWY75HypiFs6+gRBEKwhosoHMVMgrZzRUXekgDiBc3i9alXYiBLCCvviS6PGSNJZBGbNrkuF41a9on7bedxulQCUP9WF7DyKDg+mW7s2puCgAMrNK+Ai+1NpWexdpbVowDy8g6czqH61qvT+wu2m66z0QgDfaaMw6RdyqGHNCJdO8xBhsOW0j5DxUbuE4uBrYlAQBKG8EFHlg1gpkFY+T4j4IPWFiA3cwP9v1V67YFGDhVU05tx54wiMtnDcqlcUIkr6NFzqhWyqGhrEx0zNzKXN249y+m6SC7GI12EO3ygzdVYXHzNyKddGYVCU7cxpXn8+tZ+BdMgJgiAIWkRU+RhWCqRRl4QaITiXI711VWI9l9GYwlEiRAPax7MY06Oty5o+N9lUjRaK0DFbD95QsDJQaTik7v7ce4KGD0i0d5tBpJgVNxg+7G6WHq/DhJiByDRbcK7MQKVDThAEQdAjosrHsFIgrZzRUYBtRbAk1Iniwcv6Qm5VtxQZHszbHTt7gf4+kW5oDqotQofIgdmm8qdClAvRM7icq7QYbk9a6KaDuDGq9+HUnoUC6uIUnKsUmHTICYIgCFpEVPkYVgukURSOaNWxcxdMR7eiqwTTA1e3pMc/XVNkG9QtZWTlcs0Thg3DywrionpEqL2wHPePn8tkCwUUw5tJi7mq5XIlbkpa71OcgnPta6VDThAEQVCIqPIxrBZIIzpkde7fLV0aU8u4aC4mN4rAdG9Rm+f8fbFiT5F9ICoGP6yZK/daSouVRNyUhJIWnEuHnCAIgqAQUeVjWC2QPpF6gRrVirQkWNCF5+ciAgPPKWf7s+qHVd7ddJ4qOJcOOUEQBEFElY9htUC62sU5flYES05uPgUF+rM4MIrAuBNAWr8oZ7VWRr5P5dFNJwXngiAIgqco2msueD2qQBrpOT9D0XEpEgQ7ASVY3MkQ5SkFUaNNX2Ef2lt3+1N+USM/Wk1R4cEsyh4bmMh1UKrAXJ8W04obVyhx48m0mpXzKQiCIAjOkEiVD2K1QNpKNAaeUu4wu7/4GlUpLPjSj5i7tFh5ddNJwbkgCILgCfxs8ud3mZGWlkZRUVGUmppKkZGRpTb7z+jir1zYeaafC08p+FQpHyZXaPd38FQGXauZmYfi+JRT6Vy0rl2PmbUq8QLvqPIQN2bPpyAIglB5SDN5/RZR5eOiygrOBEtxDDPV/lSqEJ5ViFoZzcxT26jjupurp/Yt4kYQBEHwBkRUeSHlLaqcCRZIFTPRKWfRqik/Jxdxacee1Mw8fD/ZzTYimgRBEARfv35LTVUlxFPt/1ZmEKacyijS1edsrp4gCIIg+CLS/ScUC0SozM7MO3r2Aru6u9rmRGqmdNcJgiAIPo2IKqFMZhBinI270TPYpyAIgiD4KiKqhDKbQehuG08ZegqCIAhCeSCiSiizGYTutpFIlSAIguDLiKiyyNtvv00NGzak0NBQ6ty5M61bt44qI1Zd2n/ffbzMRs8IgiAIQnkgosoCX3/9NY0ZM4aef/552rBhA7Vt25b69+9PJ06coMqGlbEycGn/+c+/y3T0jCAIgiCUNSKqLDB58mR64IEH6N5776VWrVrRe++9R+Hh4fTxxx9TZcTszLyCggJqUKOqzNUTBEEQKjRi/mmSnJwcFlDffvst3XDDDfbHhw0bRufOnaOffvqpyGuys7P5S2seVr9+/XI3//QkZsbKgPIcPSMIgiAIJUHMPz3MqVOnKD8/n2rVcrQGwP2dO3cavmbChAn04osvUkVGiaGakaF0R88Eh7Ey2ufNbiMIgiAIvoqk/0qRp59+mlWt+jp48CBVaJd2f3+H2+JsIwiCIAi+ikSqTFK9enUKCAig48cdu9hwv3bt2oavCQkJ4S9BEARBECo+EqkySXBwMHXs2JEWL15sfwwF2LjftWvXcl2bIAiCIAjlj0SqLAA7BRSmX3bZZdSpUyeaOnUqnT9/nrsBBUEQBEGo3IiossCQIUPo5MmTNH78eDp27Bi1a9eOFixYUKR4XRAEQRCEyodYKnhhS6YgCIIgCL53/ZaaKkEQBEEQBA8gokoQBEEQBMEDiKgSBEEQBEHwAFKoXoao8jXkZgVBEARB8A3UddtdGbqIqjIkPT2dbzH/TxAEQRAE37uOo2DdGdL9V4bALPTIkSMUEREhI1osooZRY9SPdE56BjmnnkfOqeeRc+p55JxaB1IJgqpu3brk7++8ckoiVWUIPoi4uLjyXoZPg18A8kvAs8g59TxyTj2PnFPPI+fUGq4iVAopVBcEQRAEQfAAIqoEQRAEQRA8gIgqwScICQmh559/nm8FzyDn1PPIOfU8ck49j5zT0kMK1QVBEARBEDyARKoEQRAEQRA8gIgqQRAEQRAEDyCiShAEQRAEwQOIqBIEQRAEQfAAIqqEMmPFihU0ePBgdqSFo/yPP/7o8Dx6JsaPH0916tShsLAw6tu3L+3Zs8dhmzNnztCdd97JhnXR0dF0//33U0ZGhsM2W7ZsoZ49e1JoaCi7Br/++utUEZkwYQJdfvnl7NBfs2ZNuuGGG2jXrl0O22RlZdHw4cOpWrVqVLVqVbr55pvp+PHjDtukpKTQoEGDKDw8nPczduxYysvLc9hm2bJl1KFDB+4WSkhIoE8++YQqIu+++y4lJSXZTRG7du1K8+fPtz8v57PkvPrqq/zvf9SoUfbH5Lxa44UXXuBzqP1q0aKF/Xk5n+UIuv8EoSyYN2+e7d///rft+++/R8ep7YcffnB4/tVXX7VFRUXZfvzxR9vmzZtt1113na1Ro0a2zMxM+zbXXHONrW3btrbff//dtnLlSltCQoLt9ttvtz+fmppqq1Wrlu3OO++0bd261fZ///d/trCwMNv7779vq2j079/fNmPGDH6fmzZtsg0cONAWHx9vy8jIsG/z0EMP2erXr29bvHix7c8//7R16dLF1q1bN/vzeXl5tsTERFvfvn1tGzdu5M+oevXqtqefftq+zf79+23h4eG2MWPG2LZv32578803bQEBAbYFCxbYKhqzZ8+2zZ0717Z7927brl27bM8884wtKCiIzzGQ81ky1q1bZ2vYsKEtKSnJNnLkSPvjcl6t8fzzz9tat25tO3r0qP3r5MmT9uflfJYfIqqEckEvqgoKCmy1a9e2vfHGG/bHzp07ZwsJCWFhBPAPG6/7448/7NvMnz/f5ufnZzt8+DDff+edd2wxMTG27Oxs+zbjxo2zNW/e3FbROXHiBJ+f5cuX288fBMGsWbPs2+zYsYO3WbNmDd/HL1N/f3/bsWPH7Nu8++67tsjISPs5fPLJJ/kXuJYhQ4awqKsM4Ofpww8/lPNZQtLT021Nmza1LVq0yNarVy+7qJLzWjxRhT8ujZDzWb5I+k/wCg4cOEDHjh3jlJ92zlLnzp1pzZo1fB+3SPlddtll9m2wPWYqrl271r7NFVdcQcHBwfZt+vfvz2mxs2fPUkUmNTWVb2NjY/l2/fr1lJub63BOkSKIj493OKdt2rShWrVqOZwvDFzdtm2bfRvtPtQ2ah8Vlfz8fPrqq6/o/PnznAaU81kykI5Cukn/3uW8Fg+URqCUonHjxlwSgXQekPNZvshAZcErgKAC2n/k6r56DrfI/WsJDAxkEaHdplGjRkX2oZ6LiYmhikhBQQHXqHTv3p0SExPt7xfiEkLU1Tk1OufqOVfb4BdwZmYm179VJJKTk1lEoS4F9Sg//PADtWrVijZt2iTns5hAnG7YsIH++OOPIs/Jz6l18Mcm6puaN29OR48epRdffJHrSLdu3Srns5wRUSUIFSQKgF+oq1atKu+l+Dy4UEFAIfL37bff0rBhw2j58uXlvSyf5eDBgzRy5EhatGgRN48IJWfAgAH279FYAZHVoEED+uabb0TslDOS/hO8gtq1a/OtvkMF99VzuD1x4oTD8+hWQUegdhujfWiPUdF49NFHac6cObR06VKKi4uzP473m5OTQ+fOnXN5Tt2dL2fboDuuIv4Cx1/56HTq2LEjd1i2bduWpk2bJuezmCAdhX+36CJDZBlfEKnTp0/n7xH9kPNaMhCVatasGe3du1d+TssZEVWCV4CUHf4RL1682P4YwsyolUIqBuAWvyjwS1qxZMkSTn3hLzW1DawbUFOgwF/IiD5UtNQf6v0hqJCewnnQpz0hCoKCghzOKWrLUHuhPadId2nFKs4XfnEi5aW20e5DbaP2UdHBz1d2dracz2LSp08fPieI/qkv1EWiDkh9L+e1ZMBWZt++fWxHIz+n5Uw5F8oLlQh0/6B9F1/40Zs8eTJ///fff9stFaKjo20//fSTbcuWLbbrr7/e0FKhffv2trVr19pWrVrF3URaSwV0vsBSYejQodwG/9VXX3FbcEW0VHj44YfZgmLZsmUOrdUXLlxwaK2GzcKSJUu4tbpr1678pW+t7tevH9syoF26Ro0ahq3VY8eO5S6it99+u8K2Vj/11FPcPXngwAH+GcR9dJcuXLiQn5fz6Rm03X9Azqs1Hn/8cf53j5/T1atXszUCLBHQAQzkfJYfIqqEMmPp0qUspvRfw4YNs9sqPPfccyyKYKXQp08f9grScvr0aRZRVatW5fbfe++9l8WaFnhc9ejRg/dRr149FmsVEaNziS94VykgSB955BG2BcAvyBtvvJGFl5a//vrLNmDAAPbzwi9m/MLOzc0t8tm1a9fOFhwcbGvcuLHDMSoS9913n61Bgwb8PnGRwc+gElRAzmfpiCo5r9aAtUGdOnX4feJ3HO7v3bvX/rycz/LDD/8r72iZIAiCIAiCryM1VYIgCIIgCB5ARJUgCIIgCIIHEFElCIIgCILgAURUCYIgCIIgeAARVYIgCIIgCB5ARJUgCIIgCIIHEFElCIIgCILgAURUCYIgCIIgeAARVYLgAfz8/OjHH380vX3Dhg1p6tSppbomX+TKK6+kUaNGlfcyKgzufs7uueceuuGGG8p0Td7OhQsX6Oabb+Y5ePh3rR9MLAiuCHT5rCAI9osPfrk6E05Hjx61NLD5jz/+oCpVqnhwhYJgnWnTpvFgbuESn376Ka1cuZJ+++03ql69OkVFRXlM4OIPBvmjoWIjokoQPEDt2rUtbV+jRo1SW0tJyM/P57/O/f0liF0ZzqWnBIO3AaGI8x8YaP0St2/fPmrZsiUlJiaWytqEio33/msXBB9N/3Xr1o3GjRvn8PzJkycpKCiIVqxYYZiWwes//PBDuvHGGyk8PJyaNm1Ks2fPdtgH7uPx0NBQ6t27N/9F7S49MXnyZGrTpg1HxerXr0+PPPIIZWRk2J//5JNPKDo6mvfdqlUrCgkJoZSUFMrOzqYnnniC6tWrx6/t3LkzLVu2zP6606dP0+23387PY704xv/93/+5PEdmX5OXl0ePPvooX/ARKXjuueccoilnz56lu+++myOD2M+AAQNoz549/FxaWhqFhYXR/PnzHfb5ww8/UEREBKd2wMGDB+kf//gHv/fY2Fi6/vrr6a+//nK5fnfn39m5dLVe8MILL1C7du0cjoWfDfyM6NN0EydOpDp16lC1atVo+PDhlJuba9/mxIkTNHjwYH7/jRo1oi+//NLl+9HuV5t+HTFiBD355JN8XvDHAtbnCvxcdOrUiX9O8P67d+9Of//9t/35V199lWrVqsXn//7776ennnrK4f0apXyxJqxN8fnnn9Nll13G+8Ca7rjjDn6/2jXgs8Dn3rFjRz73q1atooKCApowYQKfD5yXtm3b0rfffuv0vWAtkyZN4n+n2B/uA3f/HgCO17NnTz4O/q3hPJ4/f96+X5yT0aNH837xJVRMRFQJgoe588476auvvnIQAl9//TXVrVuXf+k648UXX+QL/ZYtW2jgwIG8nzNnzvBzBw4coFtuuYUvNps3b6Z//etf9O9//9vtWhAlmT59Om3bto1FwJIlS/iCqQVC47XXXmNRh+1q1qzJombNmjX8PrCeW2+9la655hq7GMjKyuKL19y5c2nr1q304IMP0tChQ2ndunVO12L2NVgnIgx4HOkpCEOsTYGL7Z9//sniBWvEecb5gsBAHcy1115LM2fOdNgnBAbOHUQNtuvfvz9foJHmWb16NVWtWpXfX05OjuHazZ5/o3Ppar1WWLp0KUdRcItzBBGHL+15gVjE8xAO77zzjoPwMAv2DeGwdu1aev311+mll16iRYsWGW4LAYxz0qtXL/45wfvD56pEwzfffMOi7JVXXuFzAEGIdVkF5+o///kPn3v88QIBrBVdCgg2iLgdO3ZQUlISC6rPPvuM3nvvPf48IGruuusuWr58ueFxvv/+e3rggQeoa9eunNLHfeDu3wM+F9xHLRaex793iCy8Tu03Li6OzyX2iy+hgmITBMEtw4YNs11//fVOn8c/pR9++IG/P3HihC0wMNC2YsUK+/Ndu3a1jRs3zn6/QYMGtilTpji8/tlnn7Xfz8jI4Mfmz5/P9/HaxMREh2P++9//5m3Onj1r+n3MmjXLVq1aNfv9GTNm8D42bdpkf+zvv/+2BQQE2A4fPuzw2j59+tiefvppp/seNGiQ7fHHHze9FqPX9OrVy9ayZUtbQUGB/TG8dzwGdu/ezetdvXq1/flTp07ZwsLCbN988w3fx+dQtWpV2/nz5/l+amqqLTQ01H4uP//8c1vz5s0djpGdnc37+OWXXwzXaeb8G51LM+t9/vnnbW3btnXYN3428DOi/fnD/by8PPtjt956q23IkCH8/a5du/g469atsz+/Y8cOfkz7c+bu5xrnv0ePHg7bXH755Q4/u1pOnz7Nx1i2bJnh8/i5f+SRRxwe69y5s8P7xTFHjhzpsA3WhLU5448//uDjpqen8/2lS5fy/R9//NG+TVZWli08PNz222+/Obz2/vvvt91+++1O9421YE1W/j1gnw8++KDD8ytXrrT5+/vbMjMzDf/NCxUTqakSBA+Deql+/fpxdASRKUQ58Ffu+++/7/J1+MtagUgBoi4q0rBr1y66/PLLHbZHysUdv/76K/+1vnPnTk6NIbKAiBEiKojagODgYIdjJycncz1Ks2bNHPaFFAjSTgDPI/qASMThw4c5woPn1T6NMPuaLl26OKRHEDVASgavRwQCUSykXxRYU/Pmzfk5gCgQUq2IDN1222303Xff8bns27cvP49ox969ezlSpQXnBREHI8yef/25NLNes7Ru3ZoCAgLs9xH1wWelPQ4igYoWLVpwOs4q2vWr4ziLeCFFiIgRIn9XX301n2NEW/Eata6HHnrI4TX4PBFNs8L69es54oXPDulUpPUA0qtItSqQIlTgM8bPOdalBT937du3N31sM/8esC5EqLQpV/ythHXi3z9qtITKgYgqQSgFkLpDTcWbb77JqSjUD+HLFRACWiAs1MWjOCBFglTYww8/TP/973/5AoiUBOpacGFRYgY1IFoRg5orXLxxIdNexAHSZOCNN97g1Bxqf1TNFupinKXPivua4gBhg1QdzjtEFW6HDBliL1rG+4P4MKo5KmkDgf5cmgEpWn0HnlFq0NM/H86wepwZM2bwz/qCBQs47fXss89yuhDi2BPvH3VJEG34wmeGzwhiCvf1PzvajlpVO4h0M2qhtKDmyixm/j1gG6SEcR70xMfHmz6W4PuIqBKEUgCFz6gtwYUGF3UUKpcERDbmzZtXxJbBFbgI4GKIKI/qQEOUyB34Kx5/mSM64awGDHVIeI+oTwE4zu7dux2iBsV9DWp5tPz+++9cII4LGv7iR7QN26AhQBXAI5Kk3Q9ELSIUqKNBHdnLL79sf65Dhw588Ue9EyJYpXX+gZn1QiQcO3aMhYUSZJs2bSIrICqF4+AzVxE1HKOsPJbwM4Ovp59+miNR+JmHqML7x3vX/vzj89SC96+tMcLPHmru0AwAEGXFOUOtFArAAeqz3KFtFkDNV0nem7t/D/iZ2r59OyUkJLgU+9iPULGRQnVBMElqaipf7LRfKAw2An8xo4AXnWtIgaDrrSTgr2BcXNBVCCECcaSKlJ1FRvALHn/xI1q2f/9+7qBCwa47kOaAKMGFEAW2SF+gaBxpRPzVDyByEI2Alw/eH9Z3/Phxl/s1+xpcBMeMGcOiAN2BWP/IkSPt+4AwQzExom5Iu0CkIRKBxxVXXHEFd4nhfaDzS5t+w2PoKsT2KFTH+0MnF6IMhw4d8tj5N7tedIahOxRF4Ug/vv3220W6F82IPhRKY50QMRBX//znPzlyVprg3EFIIb2N7raFCxdy8bZKd+Fz+/jjjzmahfP2/PPPs9DVctVVV/HPFb5wjhFZ1YpBRHogSNTPMdK6KFp3B9K76NhDcTqK73FuN2zYwPvBfbOY+feAnwv8XKMwHb8XcA5++ukne6E6QDcnugqR+j516pTp4ws+RnkXdQmCL4CiWfxz0X+hQFVfqK6YN28eP37FFVcU2Z9Robr+9VFRUVz8rPjpp59sCQkJtpCQENuVV15pe/fdd/l1qhDWiMmTJ9vq1KnDhdH9+/e3ffbZZ0WKq3EcPTk5Obbx48fbGjZsaAsKCuJ93HjjjbYtW7bYC5RRTIyC8Jo1a3KR/d133+2ymN/Ma1AgjMLmhx56yBYZGWmLiYmxPfPMMw5F5WfOnLENHTqU163eFwrC9Tz55JP8XvE+9Bw9epSPXb16dT6fjRs3tj3wwANc1O4Md+ff2bk0s17sq379+rYqVarwuv773/8WKVTXn1t9QTXeEwr/sb74+Hj+rN0VRxsVqlspGj927Jjthhtu4J+P4OBgPh7Od35+vn0bvBecZ3zu2A8+F22hOn7WHn74YVtsbCz/XEyYMKHIMWfOnMk/i3hvKH6fPXs2n/uNGzc6FKrrmzbwczN16lRuTMDPcY0aNfj8L1++3Ok50Z9XM/8eAJoErr76an6f+ByTkpL4vSvWrFnDj+E9yKW34uKH/5W3sBMEwTqok0LkyVm0TChd5PwXDxScwxbBaopTEHwBqakSBB8B/j6ol0HHEeqTUPitTS8IpYucf0EQ3CGiShB8BNRpoOAahqCoM3n88ce5nkUoG+T8C4LgDkn/CYIgCIIgeADp/hMEQRAEQfAAIqoEQRAEQRA8gIgqQRAEQRAEDyCiShAEQRAEwQOIqBIEQRAEQfAAIqoEQRAEQRA8gIgqQRAEQRAEDyCiShAEQRAEgUrO/wNmd8m7cSj+0AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y, c='steelblue', edgecolor='white', s=70)\n",
    "plt.scatter(X, tree.predict(X), color='k')\n",
    "plt.xlabel('Living area above ground in square feet')\n",
    "plt.ylabel('Sale price in U.S. dollars')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see in the resulting plot, the decision tree captures the general trend in the data. However, a limitation of this model is that it does not capture the continuity of the desired prediction. In addition, we need to be careful about choosing an appropriate value for the depth of the tree so as to not overfit or underfit the data. Here, a depth of three seemed to be a good choice.\n",
    "Note that there are only 9 possible target values predicted by this tree (by counting the different y values of the predictions, black points). This means that the tree has 9 leaves. We can confirm this by using the *apply* method for the tree, which returns the index of the leaf that each sample is predicted as. To get only the unique value of these indices, we will use *set* method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{3, 4, 6, 7, 10, 11, 13, 14}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaves_index = tree.apply(X) \n",
    "set(leaves_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the numbers are not consecutive, as not all of the 15 nodes of the tree are leaf nodes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice question\n",
    "Load the iris dataset, split it into train and test, use cross validation on the train set to tune the tree hyperparameters. Evaluate the performance and visualize the selected tree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First install `pip install xgboost lightgbm catboost`. \n",
    "\n",
    "If incompatibilities between xgboost and scikit-learn arise, please update to the latest versions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of ensemble methods is to combine different classifiers into a meta-classifier that has better generalization performance than each individual classifier alone. There are several different \n",
    "approaches for creating an ensemble of classifier, as we will see throughout this notebook.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Voting classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A very simple way to create a better classifier is to aggregate the predictions of each classifier and predict the class that gets the most votes. The *VotingClassifier* takes in a list of different estimators as arguments and a voting method. The *hard* voting method uses the predicted labels and a majority rules system, while the *soft* voting method chooses a class that gives the largest predicted value of the sum () of the predicted probabilities.\n",
    "\n",
    "Let's first make a small synthetic dataset, and split it into train and test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "X, y = make_classification(n_samples=1000, n_features=2, n_redundant=0,  n_classes=2,\n",
    "                            n_clusters_per_class=1,   class_sep=0.2, random_state=123)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import VotingClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After defining the classifiers, we need to fit the resulting ensemble classifier object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;logreg&#x27;,\n",
       "                              LogisticRegression(C=1, solver=&#x27;liblinear&#x27;)),\n",
       "                             (&#x27;tree&#x27;,\n",
       "                              DecisionTreeClassifier(max_depth=3,\n",
       "                                                     random_state=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>VotingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.VotingClassifier.html\">?<span>Documentation for VotingClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>VotingClassifier(estimators=[(&#x27;logreg&#x27;,\n",
       "                              LogisticRegression(C=1, solver=&#x27;liblinear&#x27;)),\n",
       "                             (&#x27;tree&#x27;,\n",
       "                              DecisionTreeClassifier(max_depth=3,\n",
       "                                                     random_state=0))])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>logreg</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=1, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><label>tree</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(max_depth=3, random_state=0)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "VotingClassifier(estimators=[('logreg',\n",
       "                              LogisticRegression(C=1, solver='liblinear')),\n",
       "                             ('tree',\n",
       "                              DecisionTreeClassifier(max_depth=3,\n",
       "                                                     random_state=0))])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting = VotingClassifier(\n",
    "    [('logreg', LogisticRegression(C=1, solver='liblinear')),\n",
    "     ('tree', DecisionTreeClassifier(max_depth = 3, random_state=0))],\n",
    "    voting='hard')\n",
    "voting.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One attribute of the `VotingClassifier` is `estimators_` which returns the collection of fitted sub-estimators, in the order we specified them.\n",
    "Let's check the accuracy of the individual estimators and the ensemble. \n",
    "Recall that `sklearn` estimators have a  `score` method providing a default evaluation criterion for the problem they are designed to solve. In the case of each of the classifiers above, the `score` returns the accuracy on the given test data and labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression 0.792\n",
      "Accuracy of Decision Tree 0.776\n",
      "Accuracy of the voting ensemble 0.8\n"
     ]
    }
   ],
   "source": [
    "lr, tree = voting.estimators_\n",
    "print('Accuracy of Logistic Regression', lr.score(X_test, y_test))\n",
    "print('Accuracy of Decision Tree', tree.score(X_test, y_test))\n",
    "print('Accuracy of the voting ensemble',voting.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we will use *DecisionBoundaryDisplay.from_estimator* to visualize the decision boundary of the subestimators and the ensemble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=3, figsize=(15, 5))\n",
    "\n",
    "for classifier, ax in zip([lr, tree, voting], axs):\n",
    "        disp = DecisionBoundaryDisplay.from_estimator(classifier, X_test, response_method=\"predict\", alpha=0.5, ax=ax)\n",
    "        scatter=ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, edgecolor=\"k\")\n",
    "        handles, labels = scatter.legend_elements()\n",
    "        ax.legend(handles=handles, labels=[\"Class 0\", \"Class 1\"], title=f\"{classifier.__class__.__name__}\", frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we used  the expression `estimator.__class__.__name__ ` to obtain the name of the class to which an estimator (a model or transformer) belongs, as a string.\n",
    "Looking at the plots, we can see the liner decision boundary of the logistic regression, decision boundary of the tree orthogonal to the axes, and the final decision boundary of the voting classifier, as a combination of the two classifiers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If all classifiers are able to estimate class probabilities (i.e., they have a *predict_proba()* method), we can use *sklearn* to predict the class with the highest class probability, averaged over all the individual classifiers. This is called *soft* voting. It often achieves higher performance than hard voting because it gives more weight to highly confident votes. All we need to do is replace *voting=\"hard\"* with *voting=\"soft\"* and ensure that all classifiers can estimate class probabilities. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression 0.792\n",
      "Accuracy of Decision Tree 0.776\n",
      "Accuracy of the voting ensemble 0.8\n"
     ]
    }
   ],
   "source": [
    "voting = VotingClassifier(\n",
    "    [('logreg', LogisticRegression(C=1,solver='liblinear')),\n",
    "     ('tree', DecisionTreeClassifier(max_depth=3, random_state=0))],\n",
    "    voting='soft')\n",
    "voting.fit(X_train, y_train)\n",
    "lr, tree = voting.estimators_\n",
    "print('Accuracy of Logistic Regression', lr.score(X_test, y_test))\n",
    "print('Accuracy of Decision Tree', tree.score(X_test, y_test) )\n",
    "print('Accuracy of the voting ensemble', voting.score(X_test, y_test) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case using class probabilities gave same results as using labels, and our voting classifier still has higher accuracy than the base classifiers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following follows: https://jakevdp.github.io/PythonDataScienceHandbook/05.08-random-forests.html\n",
    "\n",
    "One way to get a diverse set of classifiers is to use very different training algorithms as just discussed. Another approach is to use the same training algorithm for every predictor, but to train them on different random subsets of the training set. When sampling is performed with replacement, this method is called bagging (short for bootstrap aggregating). Bagging allows training instances to be sampled several times for the same predictor.\n",
    "\n",
    "Once all predictors are trained, the ensemble can make a prediction for a new instance by simply aggregating the predictions of all predictors. The aggregation function is typically the statistical mode (i.e., the most frequent prediction, just like a hard voting classifier) for classification, or the average for regression. Generally, the net result is that the ensemble has a similar bias but a lower variance than a single predictor trained on the original training set.\n",
    "Bagging makes use of an ensemble  of parallel estimators, each of which overfits the data, and averages the results to find a better classification. An ensemble of randomized decision trees is known as a random forest.\n",
    "\n",
    "Let's first generate and visualize a small synthetic dataset with two features using `make_blobs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_blobs(n_samples=300, centers=4, random_state=0, cluster_std=1.0)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='rainbow');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check number of members of each class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 75), (1, 75), (2, 75), (3, 75)]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(Counter(y).items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fit a simple decision tree and visualize the decision boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Single decision tree')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "ax = plt.axes()\n",
    "tree = DecisionTreeClassifier()\n",
    "tree.fit(X, y)\n",
    "disp = DecisionBoundaryDisplay.from_estimator(tree, X, response_method=\"predict\", alpha=0.5, ax=ax)\n",
    "scatter=ax.scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\")\n",
    "handles, labels = scatter.legend_elements()\n",
    "ax.set_title(\"Single decision tree\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bagging classification can be done manually using *sklearn*'s *BaggingClassifier* meta-estimator, as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ensemble of trees by bagging')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier()\n",
    "bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8, random_state=1)\n",
    "bag.fit(X, y)\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "ax = plt.axes()\n",
    "disp = DecisionBoundaryDisplay.from_estimator(bag, X, response_method=\"predict\", alpha=0.5, ax=ax)\n",
    "scatter=ax.scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\")\n",
    "handles, labels = scatter.legend_elements()\n",
    "ax.set_title(\"Ensemble of trees by bagging\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we have randomized the data by fitting each estimator with a random subset of 80% of the training points. Then the final decision is based on voting (since this is classification, and if it was regression, it would be based on averaging). The default value of the parameter *bootstrap* is True, meaning, unless specified differently, samples are drawn with replacement. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out-of-Bag evaluation\n",
    "\n",
    "With bagging, some instances may be sampled several times for any given predictor, while others may not be sampled at all. By default a `BaggingClassifier` samples `max_samples` training instances with replacement (`bootstrap=True`), where `max_samples` is the size of the training set (can be set as a number or as a percentage of samples). This means that only about 63% of the training instances are sampled on average for each predictor. The remaining around 37% of the training instances that are not sampled are called out-of-bag (oob) instances. Note that they are not the same 37% for all predictors.\n",
    "\n",
    "This is because the probability of selecting a given instance is $\\dfrac{1}{n}$, and the probability of not selecting said instance is $\\left(1 - \\dfrac{1}{n}\\right)$; then the probability of the instance not being selected n times, i.e., not being selected at all for any predictor during bootstrap sampling is $\\left(1 - \\dfrac{1}{n}\\right)^n$. As $n$ approaches infinity, this expression converges to $\\dfrac{1}{e} \\approx 0.37$.\n",
    "\n",
    "\n",
    "Since a predictor never sees the oob instances during training, it can be evaluated on these instances, without the need for a separate validation set or cross-validation. We can evaluate the ensemble itself by averaging out the oob evaluations of each predictor. Note that, while  the oob score is calculated using all the trees in the `BaggingClassifier`, each data instance's oob prediction is only made from the trees that did not have that instance in their bootstrap sample. This means that each instance is predicted by a subset of all trees, specifically those for which the instance was out-of-bag. The final oob score is an aggregation (e.g., mean) of these individual oob predictions.\n",
    "\n",
    "In `sklearn`, we can set `oob_score=True` when creating a `BaggingClassifier` to request an automatic oob evaluation after training. The following code demonstrates this. The resulting evaluation score is available through the `oob_score_` variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag = BaggingClassifier(tree, n_estimators=100, max_samples=0.8, random_state=1,  oob_score=True)\n",
    "bag.fit(X, y)\n",
    "bag.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this is different than if we simply checked the score of the bagging classifier on the training set X:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9966666666666667"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag.score(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous example, we have randomized the data by fitting each estimator with a random subset of 80% of the training points. In practice, decision trees are more effectively randomized by injecting some stochasticity in how the splits are chosen.\n",
    "\n",
    "In *sklearn*, such an optimized ensemble of randomized decision trees is implemented in the *RandomForestClassifier* estimator, which takes care of all the randomization automatically. When splitting each node during the construction of a tree, the best split is found either from all input features or a random subset of defined by the input parameter, *max_features* (default values is square root of the number of features). The *sklearn* implementation combines classifiers by averaging their probabilistic prediction, instead of letting each classifier vote for a single class.\n",
    "\n",
    "Random Forest implementation also features the parallel construction of the trees and the parallel computation of the predictions through the n_jobs parameter. If *n_jobs=k* then computations are partitioned into k jobs, and run on k cores of the machine. If *n_jobs=-1* then all cores available on the machine are used. Note that because of inter-process communication overhead, the speedup might not be linear (i.e., using k jobs will unfortunately not be k times as fast). Significant speedup can still be achieved though when building a large number of trees, or when building a single tree requires a fair amount of time (e.g., on large datasets).  By default, *n_jobs* parameter has value 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Random Forest')"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100, random_state=0)\n",
    "model.fit(X, y)\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "ax = plt.axes()\n",
    "disp = DecisionBoundaryDisplay.from_estimator(model, X, response_method=\"predict\", alpha=0.5, ax=ax)\n",
    "scatter=ax.scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\")\n",
    "handles, labels = scatter.legend_elements()\n",
    "ax.set_title(\"Random Forest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the hand-written digits data to see how the random forest classifier can be used in this context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'feature_names', 'target_names', 'images', 'DESCR'])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "digits.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the first few digits and their labels, similarly to what we did in Notebook 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "fig, axs = plt.subplots(2, 5, figsize=(10, 5))\n",
    "for ax, img, target in zip(axs.flat, digits.images, digits.target):\n",
    "    ax.imshow(img, cmap='Greys')\n",
    "    ax.axis('off')\n",
    "    ax.text(0, 0, str(target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's use Random Forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, random_state=0)\n",
    "model = RandomForestClassifier(n_estimators=1000, oob_score=True)\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take a look at the classification report for this classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.99        38\n",
      "           1       0.98      0.98      0.98        43\n",
      "           2       0.95      1.00      0.98        42\n",
      "           3       0.98      0.96      0.97        46\n",
      "           4       0.97      1.00      0.99        37\n",
      "           5       0.98      0.96      0.97        49\n",
      "           6       1.00      1.00      1.00        52\n",
      "           7       1.00      0.96      0.98        50\n",
      "           8       0.94      0.98      0.96        46\n",
      "           9       0.98      0.98      0.98        47\n",
      "\n",
      "    accuracy                           0.98       450\n",
      "   macro avg       0.98      0.98      0.98       450\n",
      "weighted avg       0.98      0.98      0.98       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also visualize the confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x19b7a677410>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "cm=confusion_matrix(y_test, y_pred)\n",
    "ConfusionMatrixDisplay(cm).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now check out of the estimate of generalization accuracy using out-of-bag samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9732739420935412"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that oob samples give us a good estimate: 0.976 is oob_score and accuracy on the test set is 0.98."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find that a simple, untuned random forest results in a very accurate classification of the digits data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boosting\n",
    "The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor.\n",
    "There are many boosting methods available. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient Boosting\n",
    "\n",
    "Gradient Boosting works by sequentially adding predictors to an ensemble, each one correcting its predecessor, by fitting the new predictor to the residual errors made by the previous predictor.\n",
    "\n",
    "First, let's create a small synthetic dataset using `make_classification`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 10) (1000,)\n"
     ]
    }
   ],
   "source": [
    "X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=5, random_state=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=49)\n",
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *sklearn* library provides the GBM algorithm for regression and classification via the `GradientBoostingClassifier` and `GradientBoostingRegressor` classes. \n",
    "\n",
    "Much like the `RandomForestClassifier` class, it has hyperparameters to control the growth of Decision Trees (e.g., max_depth, min_samples_leaf, ...), as well as hyperparameters to control the ensemble training, such as the number of trees (n_estimators). \n",
    "\n",
    "Let's first fit `GradientBoostingClassifier` without any parameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9762609366958311"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "gbc = GradientBoostingClassifier(random_state=42)\n",
    "gbc.fit(X_train, y_train)\n",
    "y_pred_prob= gbc.predict_proba(X_test)\n",
    "roc_auc_score(y_test, y_pred_prob[:,1]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's tune the model using `RandomizedSearchCV` and check the test error. Note that we are checking the test error for illustrative purposes, and not to do model selection. In practice, **test error is evaluated only once for the final selected model, and no modeling decisions should be based on the test set**.\n",
    "\n",
    "Recall that in `RandomizedSearchCV`, in contrast to `GridSearchCV`, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by *n_iter*.\n",
    "The parameter *n_iter* trades off runtime vs quality of the solution. Here we are not going to use large value of *n_iter* so the code should run relatively quickly. In practice, this value should be higher and more parameters should be optimized, and wider parameter range should be sampled.\n",
    "\n",
    "Additionally, here we are optimizing using the *scoring='roc_auc'*. The scoring method should be chose carefully, depending on the problem we are solving.\n",
    "\n",
    "While in this notebook, for simplicity we will only use `RandomizedSearchCV`, there are other more sophisticated methods for parameter tuning, which we covered in the Notebook 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=GradientBoostingClassifier(), n_iter=20,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;learning_rate&#x27;: [0.1, 0.05, 0.03,\n",
       "                                                          0.01],\n",
       "                                        &#x27;max_depth&#x27;: [3, 5, 7],\n",
       "                                        &#x27;max_features&#x27;: [0.6, 0.8, 1.0],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 3, 5],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200]},\n",
       "                   random_state=42, scoring=&#x27;roc_auc&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomizedSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, estimator=GradientBoostingClassifier(), n_iter=20,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={&#x27;learning_rate&#x27;: [0.1, 0.05, 0.03,\n",
       "                                                          0.01],\n",
       "                                        &#x27;max_depth&#x27;: [3, 5, 7],\n",
       "                                        &#x27;max_features&#x27;: [0.6, 0.8, 1.0],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 3, 5],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 200]},\n",
       "                   random_state=42, scoring=&#x27;roc_auc&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: GradientBoostingClassifier</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(learning_rate=0.03, max_depth=7, max_features=0.6,\n",
       "                           min_samples_leaf=2, n_estimators=200)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>GradientBoostingClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html\">?<span>Documentation for GradientBoostingClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>GradientBoostingClassifier(learning_rate=0.03, max_depth=7, max_features=0.6,\n",
       "                           min_samples_leaf=2, n_estimators=200)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=GradientBoostingClassifier(), n_iter=20,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'learning_rate': [0.1, 0.05, 0.03,\n",
       "                                                          0.01],\n",
       "                                        'max_depth': [3, 5, 7],\n",
       "                                        'max_features': [0.6, 0.8, 1.0],\n",
       "                                        'min_samples_leaf': [1, 2, 3, 5],\n",
       "                                        'n_estimators': [100, 200]},\n",
       "                   random_state=42, scoring='roc_auc')"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV  \n",
    "param_grid = {'learning_rate': [0.1,0.05,0.03,0.01],                              \n",
    "              'max_depth': [3,5,7],               \n",
    "              'max_features': [0.6,0.8,1.0],               \n",
    "              'min_samples_leaf': [1,2,3,5],               \n",
    "              'n_estimators': [100, 200]            }  \n",
    "random_search = RandomizedSearchCV(GradientBoostingClassifier(), param_distributions = param_grid, cv=5, \n",
    "                                   n_iter=20, random_state=42,scoring='roc_auc', n_jobs=-1) \n",
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the best parameters found by randomized search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 200, 'min_samples_leaf': 2, 'max_features': 0.6, 'max_depth': 7, 'learning_rate': 0.03}\n"
     ]
    }
   ],
   "source": [
    "print(random_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's evaluate the ROC score of the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9879053010808029"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_prob= random_search.best_estimator_.predict_proba(X_test)\n",
    "roc_auc_score(y_test, y_pred_prob[:,1]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram-Based Gradient Boosting\n",
    "The `sklearn` library provides an alternate implementation of the gradient boosting algorithm, referred to as histogram-based gradient boosting.\n",
    "\n",
    "This is an alternate approach to implement gradient tree boosting inspired by the `LightGBM` library. This implementation is provided via the `HistGradientBoostingClassifier` and `HistGradientBoostingRegressor`.\n",
    "\n",
    "The primary benefit of the histogram-based approach to gradient boosting is speed. These implementations are designed to be much faster to fit on training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.975102933607823"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "hgbr = HistGradientBoostingClassifier()\n",
    "hgbr.fit(X_train, y_train)\n",
    "y_pred_prob= hgbr.predict_proba(X_test)\n",
    "roc_auc_score(y_test, y_pred_prob[:,1]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with `GradientBoostingClassifier`, parameters of `HistGradientBoostingClassifier` should be carefully tuned.\n",
    "Please check the documentation for the list of parameters:\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost\n",
    "XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. It is one of the most popular libraries in practice, since with careful tuning, it often has superior performance compared to other algorithms when applied to structured data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are some XGBoost specifics:\n",
    "\n",
    "- Parallelized Tree Building: In boositing, trees are built sequentially and thus cannot be created in parallel as in the Random Forest algorithm; however, XGBoost still leverages parallelism—thereby gaining speed—by adding it to the construction of individual trees: within a single tree, XGBoost parallelizes the creation of branches, since each node in the decision tree corresponds to a particular feature and a split point and, therefore, XGBoost can evaluate potential splits for different features concurrently using multiple CPU cores.\n",
    "<div style=\"margin-top: 10px;\"></div>\n",
    "\n",
    "- Regularization: In contrast to GBM, XGBoost implements the L1 Regularization (Lasso) and L2 Regularization (Ridge) methods that allow it to address overfitting better than its predecessor.\n",
    "<div style=\"margin-top: 10px;\"></div>\n",
    "\n",
    "- Missing Data: XGBoost implemented has a technique to handle missing data called Sparsity-aware Split Finding: for any sparsities data that XGBoost encounters,for each split XGBoost essentially tries both possibilities (assigning instances with missing values to the left or the right child of the node) and chooses the direction that yields the highest gain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9758749356664952"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb = XGBClassifier()\n",
    "xgb.fit(X_train, y_train)\n",
    "y_pred_prob= xgb.predict_proba(X_test)\n",
    "roc_auc_score(y_test, y_pred_prob[:,1]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Xgboost has many hyperparameters, full list is available at:\n",
    "    \n",
    "https://xgboost.readthedocs.io/en/latest/parameter.html#general-parameters\n",
    "\n",
    "Much attention should be given to model tuning. However, here we are only going to use a small randomized search.\n",
    "An example of more detailed tuning is available at:\n",
    "https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: to sample from values uniformly, we will use `scipy.stats.uniform` function and using the parameters `loc` and `scale`, we can obtain the uniform distribution on `[loc, loc + scale]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None, device=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate...\n",
       "                                        &#x27;colsample_bytree&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x0000019B7A4A36E0&gt;,\n",
       "                                        &#x27;gamma&#x27;: [0, 0.1, 0.25, 0.5, 1.0],\n",
       "                                        &#x27;learning_rate&#x27;: [0.001, 0.01, 0.1, 0.2,\n",
       "                                                          0, 3],\n",
       "                                        &#x27;max_depth&#x27;: [6, 10, 15, 20],\n",
       "                                        &#x27;min_child_weight&#x27;: [0.5, 1.0, 3.0,\n",
       "                                                             5.0],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 150, 200, 300,\n",
       "                                                         400],\n",
       "                                        &#x27;reg_lambda&#x27;: [0, 0.1, 1, 2, 5, 7, 10],\n",
       "                                        &#x27;subsample&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x0000019B7A5DB830&gt;},\n",
       "                   random_state=42, scoring=&#x27;roc_auc&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomizedSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None, device=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate...\n",
       "                                        &#x27;colsample_bytree&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x0000019B7A4A36E0&gt;,\n",
       "                                        &#x27;gamma&#x27;: [0, 0.1, 0.25, 0.5, 1.0],\n",
       "                                        &#x27;learning_rate&#x27;: [0.001, 0.01, 0.1, 0.2,\n",
       "                                                          0, 3],\n",
       "                                        &#x27;max_depth&#x27;: [6, 10, 15, 20],\n",
       "                                        &#x27;min_child_weight&#x27;: [0.5, 1.0, 3.0,\n",
       "                                                             5.0],\n",
       "                                        &#x27;n_estimators&#x27;: [100, 150, 200, 300,\n",
       "                                                         400],\n",
       "                                        &#x27;reg_lambda&#x27;: [0, 0.1, 1, 2, 5, 7, 10],\n",
       "                                        &#x27;subsample&#x27;: &lt;scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x0000019B7A5DB830&gt;},\n",
       "                   random_state=42, scoring=&#x27;roc_auc&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: XGBClassifier</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=0.6562646731757538, colsample_bynode=None,\n",
       "              colsample_bytree=0.8908088595534959, device=None,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=15,\n",
       "              max_leaves=None, min_child_weight=1.0, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=200,\n",
       "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=0.6562646731757538, colsample_bynode=None,\n",
       "              colsample_bytree=0.8908088595534959, device=None,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, feature_types=None, gamma=0, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=0.1, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=15,\n",
       "              max_leaves=None, min_child_weight=1.0, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=200,\n",
       "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           callbacks=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None, device=None,\n",
       "                                           early_stopping_rounds=None,\n",
       "                                           enable_categorical=False,\n",
       "                                           eval_metric=None, feature_types=None,\n",
       "                                           gamma=None, grow_policy=None,\n",
       "                                           importance_type=None,\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate...\n",
       "                                        'colsample_bytree': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x0000019B7A4A36E0>,\n",
       "                                        'gamma': [0, 0.1, 0.25, 0.5, 1.0],\n",
       "                                        'learning_rate': [0.001, 0.01, 0.1, 0.2,\n",
       "                                                          0, 3],\n",
       "                                        'max_depth': [6, 10, 15, 20],\n",
       "                                        'min_child_weight': [0.5, 1.0, 3.0,\n",
       "                                                             5.0],\n",
       "                                        'n_estimators': [100, 150, 200, 300,\n",
       "                                                         400],\n",
       "                                        'reg_lambda': [0, 0.1, 1, 2, 5, 7, 10],\n",
       "                                        'subsample': <scipy.stats._distn_infrastructure.rv_continuous_frozen object at 0x0000019B7A5DB830>},\n",
       "                   random_state=42, scoring='roc_auc')"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import uniform as sp_uniform\n",
    "param_grid= {\n",
    "        'max_depth': [6, 10, 15, 20],\n",
    "        'learning_rate': [0.001, 0.01, 0.1, 0.2, 0,3],\n",
    "        'subsample': sp_uniform(loc=0.2, scale=0.8),\n",
    "        'colsample_bytree': sp_uniform(loc=0.4, scale=0.6),\n",
    "        'colsample_bylevel': sp_uniform(loc=0.4, scale=0.6),\n",
    "        'min_child_weight': [0.5, 1.0, 3.0, 5.0],\n",
    "        'gamma': [0, 0.1, 0.25, 0.5, 1.0],\n",
    "        'reg_lambda': [0, 1e-1, 1, 2, 5, 7, 10],\n",
    "        'n_estimators': [100,150, 200,300, 400]\n",
    "        }\n",
    "xgb = XGBClassifier()\n",
    "random_search = RandomizedSearchCV(xgb, param_distributions = param_grid,cv=5,n_iter=20, random_state=42, scoring='roc_auc', n_jobs=-1) \n",
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the best parameters found by the randomized search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'colsample_bylevel': 0.6562646731757538, 'colsample_bytree': 0.8908088595534959, 'gamma': 0, 'learning_rate': 0.1, 'max_depth': 15, 'min_child_weight': 1.0, 'n_estimators': 200, 'reg_lambda': 0, 'subsample': 0.5878639770871865}\n"
     ]
    }
   ],
   "source": [
    "print(random_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the metric on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9740735975295934"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_prob= random_search.best_estimator_.predict_proba(X_test)\n",
    "roc_auc_score(y_test, y_pred_prob[:,1]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LightGBM\n",
    "\n",
    "LightGBM is another gradient boosting framework that uses tree based learning algorithms and is widely used in practice. It is designed to be distributed and efficient. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LightGBM benefits from enhancements that were also included in XGBoost (regularizations, handling of missing data, etc.), and it introduces additional optimizations that set it apart. Some of the most important are the following:\n",
    "\n",
    "- \"Leaf-wise\" Criterion: LightGBM adopts a leaf-wise growth stategy, in contrast to the usual level-wise approach whereby all nodes are expanded at each level. This is faster than the level-wise method and tends to culminates in deeper but narrower trees that can offer flexibility to capture complex feature relationships, but it can also lead to overfitting.\n",
    "\n",
    "-  Generally, LightGBM is faster and uses less memory than XGBoost, particularly because of its leaf-wise tree growth algorithm and data structure optimizations. \n",
    "\n",
    "- LightGBM natively supports categorical features, meaning it can handle them without manual preprocessing into numerical dummy variables, however these categorical features must be specified with `categorical_feature` parameter and must be encoded as integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\envs\\ml2025\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n",
      "C:\\ProgramData\\anaconda3\\envs\\ml2025\\Lib\\site-packages\\sklearn\\utils\\deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9815362840967576"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lightgbm.sklearn import LGBMClassifier\n",
    "\n",
    "lgbm = LGBMClassifier(verbosity=-1)\n",
    "lgbm.fit(X_train, y_train)\n",
    "y_pred_prob= lgbm.predict_proba(X_test)\n",
    "roc_auc_score(y_test, y_pred_prob[:,1]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LightGBM also has many parameters that need to be tuned. Please check the documentation:\n",
    "https://lightgbm.readthedocs.io/en/latest/Parameters-Tuning.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoost\n",
    "\n",
    "CatBoost is yet another gradient boosting framework that uses tree based learning algorithms and is widely used in practice.\n",
    "This library should be installed as follows:\n",
    "\n",
    "Documentation is available at:\n",
    "https://catboost.ai/en/docs/concepts/python-quickstart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Catboost shares similarities with its counterparts as well (regularizations, handling of missing data, etc.), but it also has some innovations:\n",
    "\n",
    "- CatBoost has even more support for categorical features compared to LightGBM, it uses an advanced algorithm to process categorical variables, reducing the need for extensive data preprocessing and potentially leading to better performance, especially in datasets with many categorical features. Here, there is no need for preprocessing beforehand. It employs various strategies, such as target-based encoding.\n",
    "\n",
    "- Symmetric Trees: Symmetric decision trees employ the same threshold and feature to each node on the same level of the tree. Although these trees are worse predictors, the idea of gradient boosting is to combine weak learners to arrive at an optimal decision, and these trees fulfill that criterion while being faster at predicting than normal trees. \n",
    "\n",
    "- Ordered Boosting: In order to prevent overfitting, Catboost employs ordered boosting: unlike traditional boosting algorithms that leverage the same data for residual estimation as the data used to train the model, ordered boosting divides a given dataset into random permutations and trains each learner on one subset of the data while calculating the residuals on another."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will show how to fit the *CatBoostClassifier* with no tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9817292846114256"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "\n",
    "catb = CatBoostClassifier(verbose=False, iterations=100)\n",
    "catb.fit(X_train, y_train)\n",
    "y_pred_prob= catb.predict_proba(X_test)\n",
    "roc_auc_score(y_test, y_pred_prob[:,1]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Catboost has many different parameters that should be tuned. The full list is available at:\n",
    "    \n",
    "https://catboost.ai/docs/concepts/parameter-tuning.html\n",
    "    \n",
    "As in other gradient boosting libraries, much attention and time should be given to model tuning. Here, for illustrative purposes, we are only going to optimize a few parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=&lt;catboost.core.CatBoostClassifier object at 0x0000019B791208C0&gt;,\n",
       "                   n_iter=20, n_jobs=-1,\n",
       "                   param_distributions={&#x27;depth&#x27;: [3, 5, 7],\n",
       "                                        &#x27;l2_leaf_reg&#x27;: [1, 3, 5, 7, 9],\n",
       "                                        &#x27;learning_rate&#x27;: [0.1, 0.05, 0.03,\n",
       "                                                          0.01]},\n",
       "                   random_state=42, scoring=&#x27;roc_auc&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomizedSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=&lt;catboost.core.CatBoostClassifier object at 0x0000019B791208C0&gt;,\n",
       "                   n_iter=20, n_jobs=-1,\n",
       "                   param_distributions={&#x27;depth&#x27;: [3, 5, 7],\n",
       "                                        &#x27;l2_leaf_reg&#x27;: [1, 3, 5, 7, 9],\n",
       "                                        &#x27;learning_rate&#x27;: [0.1, 0.05, 0.03,\n",
       "                                                          0.01]},\n",
       "                   random_state=42, scoring=&#x27;roc_auc&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: CatBoostClassifier</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x0000019B784AC8C0&gt;</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>CatBoostClassifier</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>&lt;catboost.core.CatBoostClassifier object at 0x0000019B784AC8C0&gt;</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=<catboost.core.CatBoostClassifier object at 0x0000019B791208C0>,\n",
       "                   n_iter=20, n_jobs=-1,\n",
       "                   param_distributions={'depth': [3, 5, 7],\n",
       "                                        'l2_leaf_reg': [1, 3, 5, 7, 9],\n",
       "                                        'learning_rate': [0.1, 0.05, 0.03,\n",
       "                                                          0.01]},\n",
       "                   random_state=42, scoring='roc_auc')"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid= {'learning_rate': [0.1,0.05,0.03,0.01],\n",
    "        'depth': [3, 5,7],\n",
    "        'l2_leaf_reg': [1, 3, 5, 7, 9] }\n",
    "random_search = RandomizedSearchCV(CatBoostClassifier(verbose=False,iterations=100), param_distributions = param_grid,\n",
    "                                   cv=5, n_iter=20, random_state=42, scoring='roc_auc', n_jobs=-1) \n",
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the best parameters found by the randomized search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.1, 'l2_leaf_reg': 1, 'depth': 7}\n"
     ]
    }
   ],
   "source": [
    "print(random_search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the metric on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9837236232629953"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_prob= random_search.best_estimator_.predict_proba(X_test)\n",
    "roc_auc_score(y_test, y_pred_prob[:,1]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imbalanced classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The learning phase and the subsequent prediction of machine learning algorithms can be affected by the problem of imbalanced data set. The balancing issue corresponds to the difference of the number of samples in the different classes. In order to demonstrate different techniques for dealing with classification with imbalanced classes, we will use the `imbalanced-learn` Python library, (imported as `imblearn`), which can be installed as:\n",
    "\n",
    "`pip install imbalanced-learn`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imblearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will again use  `make_classification` function to generate a random n-class classification problem, where, with the parameter *weights* we can specify the proportions of samples assigned to each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples=1000, n_features=2, n_redundant=0, \n",
    "                            n_classes=3, n_clusters_per_class=1,\n",
    "                            weights=[0.01, 0.05, 0.94],\n",
    "                            class_sep=0.8, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 13), (1, 55), (2, 932)]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(Counter(y).items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's fit a Logistic Regression model and see what decision boundary looks like. Note that the goal of this exercise is only to illustrate the data imbalance issue, hence we are not splitting our data set into train and test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-8 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression()\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19b7a5d8290>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "ax = plt.axes()\n",
    "disp = DecisionBoundaryDisplay.from_estimator(clf, X, response_method=\"predict\", alpha=0.5, ax=ax)\n",
    "scatter=ax.scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\")\n",
    "handles, labels = scatter.legend_elements()\n",
    "ax.legend(handles=handles, labels=[\"Class 0\", \"Class 1\",  \"Class 2\"], frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The decision function heavily favors the class with the larger number of samples, Class 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that from class 0 we have 13 examples, class 1, we have 55 examples and class 2,  we have 932 examples. This is the ratio we set using *weights* argument of *make_classification*.\n",
    "\n",
    "Next, we will showcase a few methods for data balancing, implemented in `imblearn` library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Imblearn* library follows the *sklearn* approach, with the base *estimator* and adding a sampling functionality through the sample method:\n",
    "\n",
    "- Estimator implements a *fit* method to learn from data.\n",
    "\n",
    "- Resampler implements: *fit_resample* method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Under-sampling\n",
    "Undersampling methods delete or select a subset of examples from the majority class. The simplest undersampling method involves randomly deleting examples from the majority class.\n",
    "To perform under-sampling we can use `RandomUnderSampler`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "rus = RandomUnderSampler(replacement=False, random_state=0)\n",
    "X_resampled, y_resampled = rus.fit_resample( X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter *replacement=False* ensures that once a sample is selected to be part of the under-sampled majority class, it won't be selected again; thus, even though the majority class is being reduced, individual samples cannot appear multiple times in the resampled dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the number of instances in our original dataset *X*, and our subsampled dataset *X_resampled*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 2)\n",
      "(39, 2)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(X_resampled.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now let's see the number of each example per class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 13), (1, 13), (2, 13)]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(Counter(y_resampled).items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that now all classes have the same number of examples as the minority class.\n",
    "We could have set a different ratio by setting the parameter *sampling_strategy* of the `RandomUnderSampler`. The default value of this parameter is 'not minority', meaning resampling of all classes but the minority class is done."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now visualize, side by side, the original dataset *X*, and our subsampled dataset *X_resampled*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Dataset with under-sampling')"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
    "axs[0].scatter(X[:, 0], X[:, 1], alpha=0.5, c=y)\n",
    "axs[0].set_title('Original dataset')\n",
    "\n",
    "axs[1].scatter(X_resampled[:, 0], X_resampled[:, 1], alpha=0.4, c=y_resampled)\n",
    "axs[1].set_title('Dataset with under-sampling')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now see the decision function we learnt by fitting the sampled data to Logistic Regression. Note we will use the sampled data to learn the model, but we will plot the decision boundaries and the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision boundary with under-sampling')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.fit(X_resampled, y_resampled)\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "ax = plt.axes()\n",
    "disp = DecisionBoundaryDisplay.from_estimator(clf, X, response_method=\"predict\", alpha=0.5, ax=ax)\n",
    "scatter=ax.scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\")\n",
    "handles, labels = scatter.legend_elements()\n",
    "ax.legend(handles=handles, labels=[\"Class 0\", \"Class 1\",  \"Class 2\"], frameon=False)\n",
    "ax.set_title('Decision boundary with under-sampling')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the majority class does not take over the other classes during the training process, but the training of the model is done using very few data points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Over-sampling\n",
    "\n",
    "Another way to deal with imbalanced data is to generate new samples of the classes which are under-represented. The most naive strategy is to generate new samples by randomly sampling with replacement the current available samples. The `RandomOverSampler` offers such scheme:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 2)\n",
      "(2796, 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(0, 932), (1, 932), (2, 932)]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "ros = RandomOverSampler(random_state=0)\n",
    "X_resampled, y_resampled = ros.fit_resample(X, y)\n",
    "print(X.shape)\n",
    "print(X_resampled.shape)\n",
    "sorted(Counter(y_resampled).items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, let's visualize the original and the sampled dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Dataset with over-sampling')"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
    "axs[0].scatter(X[:, 0], X[:, 1], alpha=0.4, c=y)\n",
    "axs[0].set_title('Original dataset')\n",
    "\n",
    "axs[1].scatter(X_resampled[:, 0], X_resampled[:, 1], alpha=0.4, c=y_resampled)\n",
    "axs[1].set_title('Dataset with over-sampling')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new samples generated are superimposing with the original samples, hence we see the same number of points on the second plot, but the color is darker, as multiple points are overlapping."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now see the decision boundary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision boundary with over-sampling')"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.fit(X_resampled, y_resampled)\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "ax = plt.axes()\n",
    "disp = DecisionBoundaryDisplay.from_estimator(clf, X, response_method=\"predict\", alpha=0.5, ax=ax)\n",
    "scatter=ax.scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\")\n",
    "handles, labels = scatter.legend_elements()\n",
    "ax.legend(handles=handles, labels=[\"Class 0\", \"Class 1\",  \"Class 2\"], frameon=False)\n",
    "ax.set_title('Decision boundary with over-sampling')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the majority class does not take over the other classes during the training process. Consequently, all classes are represented by the decision function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SMOTE\n",
    "Another popular methods to over-sample minority classes is the Synthetic Minority Oversampling Technique (SMOTE). While the `RandomOverSampler` is over-sampling by duplicating some of the original samples of the minority class, `SMOTE`  generates new samples in by interpolation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 2)\n",
      "(2796, 2)\n",
      "[(0, 932), (1, 932), (2, 932)]\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "X_resampled, y_resampled = SMOTE().fit_resample(X, y)\n",
    "print(X.shape)\n",
    "print(X_resampled.shape)\n",
    "print(sorted(Counter(y_resampled).items()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's see  our original and the sampled dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Dataset with SMOTE')"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))\n",
    "axs[0].scatter(X[:, 0], X[:, 1], alpha=0.4, c=y)\n",
    "axs[0].set_title('Original dataset')\n",
    "\n",
    "axs[1].scatter(X_resampled[:, 0], X_resampled[:, 1], alpha=0.4, c=y_resampled)\n",
    "axs[1].set_title('Dataset with SMOTE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the decision boundary found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Decision boundary with SMOTE')"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.fit(X_resampled, y_resampled)\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "ax = plt.axes()\n",
    "disp = DecisionBoundaryDisplay.from_estimator(clf, X, response_method=\"predict\", alpha=0.5, ax=ax)\n",
    "scatter=ax.scatter(X[:, 0], X[:, 1], c=y, edgecolor=\"k\")\n",
    "handles, labels = scatter.legend_elements()\n",
    "ax.legend(handles=handles, labels=[\"Class 0\", \"Class 1\",  \"Class 2\"], frameon=False)\n",
    "ax.set_title('Decision boundary with SMOTE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that SMOTE is based on finding the neighbors of data points based on distance calculations, hence doing scaling before applying SMOTE is recommended. When dealing with mixed data type such as continuous and categorical features, we need to use different versions of SMOTE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For dealing with categorical features, extension of SMOTE, SMOTEN (SMOTE for Nominal) is used. Let's see a quick example. We will create 60 data points, with only one feature (X), with 20 of class 0 and 40 of class 1. The single feature could take values of \"A\", \"B\" or \"C\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array([\"A\"] * 10 + [\"B\"] * 20 + [\"C\"] * 30, dtype=object).reshape(-1, 1)\n",
    "y = np.array([0] * 20 + [1] * 40, dtype=np.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the first 5 data points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['A'],\n",
       "       ['A'],\n",
       "       ['A'],\n",
       "       ['A'],\n",
       "       ['A']], dtype=object)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 20), (1, 40)]\n"
     ]
    }
   ],
   "source": [
    "print(sorted(Counter(y).items()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's apply SMOTE for nominal features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTEN\n",
    "sampler = SMOTEN(random_state=0)\n",
    "\n",
    "X_res, y_res = sampler.fit_resample(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the new dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 40), (1, 40)]\n"
     ]
    }
   ],
   "source": [
    "print(sorted(Counter(y_res).items()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now given that SMOTE is generates synthetic examples along the line segments joining k minority class nearest neighbors, how are these neighbors calculated if we only have categorical features? This is done using a special metric where categories with similar correlation with the target vector will be considered closer. A more detailed explanation can be found [here](https://imbalanced-learn.org/stable/references/generated/imblearn.metrics.pairwise.ValueDifferenceMetric.html#imblearn.metrics.pairwise.ValueDifferenceMetric)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mixed data types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When dealing with mixed data type such as continuous and categorical features, SMOTENC is used. Let's create a synthetic data set with 50 datapoints, one continuous and two categorical features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 20), (1, 30)]\n"
     ]
    }
   ],
   "source": [
    "rng = np.random.RandomState(42)\n",
    "n_samples = 50\n",
    "X = np.empty((n_samples, 3), dtype=object)\n",
    "\n",
    "# first feature can take value A, B or C\n",
    "X[:, 0] = rng.choice(['A', 'B', 'C'], size=n_samples).astype(object)\n",
    "\n",
    "# second feature is numerical\n",
    "X[:, 1] = rng.randn(n_samples)\n",
    "\n",
    "# third feature can take values 1, 2 or 3\n",
    "X[:, 2] = rng.randint(3, size=n_samples)\n",
    "\n",
    "y = np.array([0] * 20 + [1] * 30)\n",
    "print(sorted(Counter(y).items()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the first 5 data points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['C', -0.14021849735700803, 2],\n",
       "       ['A', -0.033193400066544886, 2],\n",
       "       ['C', -0.7490765234433554, 1],\n",
       "       ['C', -0.7783820070908942, 2],\n",
       "       ['A', 0.948842857719016, 2]], dtype=object)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to pass the information to the SMOTENC which features are categorical, using the parameters `categorical_features`, either by passing the indices of these features or a boolean mask marking these features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 30), (1, 30)]\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTENC\n",
    "\n",
    "smote_nc = SMOTENC(categorical_features=[0, 2], random_state=0)\n",
    "X_resampled, y_resampled = smote_nc.fit_resample(X, y)\n",
    "print(sorted(Counter(y_resampled).items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['A', 0.1989993778979113, 2],\n",
       "       ['B', -0.3657680728116921, 2],\n",
       "       ['B', 0.8790828729585258, 2],\n",
       "       ['B', 0.3710891618824609, 2],\n",
       "       ['B', 0.3327240726719727, 2]], dtype=object)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_resampled[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The samples generated in the first and last columns are belonging to the same categories originally presented without any other extra interpolation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate the distances between the data points, in order to identify neighbors, SMOTENC computes the Euclidean distance between the feature vector using the continuous feature space. For every differing nominal feature between the considered feature vector and its potential nearest-neighbor, an additional calculation is made. More details can be found [here](https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume16/chawla02a-html/node15.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Oversampling and undersampling can also be extended to regression problems when relevant values of the target are underrepresented. A case in point would be the prediction of salaries, in which mid-range salaries are likely to be prevalent, yet instances of higher incomes would be much more rare, culminating in the model performing less optimally in this bracket. \n",
    "\n",
    "Imbalanced regression is a less commonly discussed topic than imbalanced classification, but unevenly distributed targets can often occur, and specific approaches can be considered for these situations: aside from other methods like altering the target's distribution to be closer to the Gaussian's or adopting scoring functions that focus on rare instances, an example pertaining to sampling is SMOGN that removes samples of the majority class and creates new samples of the minority class through interpolation (with the addition of noise). More details can be found [here](https://github.com/nickkunz/smogn)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avoiding data leakage with pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Special attention must be paid when data set balancing is done. Data balancing is only performed on the training dataset in order for the algorithm to learns a model. \n",
    "**Balancing is not performed on the holdout test or test dataset**. \n",
    "Doing the balancing on the entire dataset before splitting it into a train and a test partitions leads to two problematic issues:\n",
    "- The resampling procedure might use information about test samples to either generate or select some of the samples for training. \n",
    "- The model will not be tested on a dataset with class distribution similar and representative to the real use-case:  misleading and perhaps overly optimistic estimation of performance.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While it is a pretty straightforward conclusion that the dataset balancing should be done only after the train test split, balancing the training set is problematic if we decide to do cross-validation. Why is that? Recall that in cross-validation, folds of the training dataset will be used for training and also for validation.  `imblearn.Pipeline` object allows us to apply dataset balancing only to the training folds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now import an unbalanced dataset using `sklearn`'s `fetch_openml`, which can fetch a dataset from https://www.openml.org/ by name or dataset id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11183, 6)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "data = fetch_openml('mammography', as_frame=True, parser='auto')\n",
    "X, y = data.data, data.target\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the class distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "-1    10923\n",
       "1       260\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's change y to have classes 0 and 1 (instead of -1 and 1) to allow *sklearn* to determine the positive class more easily. We will use `.cat.rename_categories` pandas function for renaming categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "0    10923\n",
       "1      260\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = y.cat.rename_categories({'-1': 0, '1': 1})\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in *scikit-learn*, in *imbalanced-learn*, we will be using a *Pipeline* to avoid data leakage. **A traditional *scklearn* Pipeline cannot be used with sampling methods.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.pipeline import Pipeline \n",
    "\n",
    "undersample_pipe = Pipeline([('sampling', RandomUnderSampler()), \n",
    "                             ('classifier', LogisticRegression(solver='liblinear'))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the pipeline, when used in cross validation, first splits into training and validation folds, and then applies sampling subsequently only to the training ones. When called `predict` on a `imblearn.Pipeline` object, it will skip the sampling method and leave the data as it is to be passed to next transformer. Hence,  we train the dataset on the sampled data, but evaluate on non-sampled data. You can also consult the following [link](https://kiwidamien.github.io/how-to-do-cross-validation-when-upsampling-data.html) for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check the performance of our pipeline we will use `cross_validate` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "scores = cross_validate(undersample_pipe,\n",
    "                        X_train, y_train, cv=10,\n",
    "                        scoring=('roc_auc', 'recall','precision'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Cross_validate` returns a dictionary of arrays containing the following metrics: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['fit_time', 'score_time', 'test_roc_auc', 'test_recall', 'test_precision'])"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the mean of these metrics on the hold out folds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC is 0.9182325497922417\n",
      "Recall is  0.8621052631578946\n",
      "Precision is 0.1638931157208967\n"
     ]
    }
   ],
   "source": [
    "print('ROC AUC is', scores['test_roc_auc'].mean())\n",
    "print('Recall is ', scores['test_recall'].mean())\n",
    "print('Precision is', scores['test_precision'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's put together sampling and model tuning. Recall that we can use *get_params()* to see the names of all the parameters of an estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'memory': None,\n",
       " 'steps': [('scaling', StandardScaler()),\n",
       "  ('sampling', SMOTE()),\n",
       "  ('classifier', LogisticRegression(solver='liblinear'))],\n",
       " 'transform_input': None,\n",
       " 'verbose': False,\n",
       " 'scaling': StandardScaler(),\n",
       " 'sampling': SMOTE(),\n",
       " 'classifier': LogisticRegression(solver='liblinear'),\n",
       " 'scaling__copy': True,\n",
       " 'scaling__with_mean': True,\n",
       " 'scaling__with_std': True,\n",
       " 'sampling__k_neighbors': 5,\n",
       " 'sampling__random_state': None,\n",
       " 'sampling__sampling_strategy': 'auto',\n",
       " 'classifier__C': 1.0,\n",
       " 'classifier__class_weight': None,\n",
       " 'classifier__dual': False,\n",
       " 'classifier__fit_intercept': True,\n",
       " 'classifier__intercept_scaling': 1,\n",
       " 'classifier__l1_ratio': None,\n",
       " 'classifier__max_iter': 100,\n",
       " 'classifier__multi_class': 'deprecated',\n",
       " 'classifier__n_jobs': None,\n",
       " 'classifier__penalty': 'l2',\n",
       " 'classifier__random_state': None,\n",
       " 'classifier__solver': 'liblinear',\n",
       " 'classifier__tol': 0.0001,\n",
       " 'classifier__verbose': 0,\n",
       " 'classifier__warm_start': False}"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('scaling', StandardScaler()), \n",
    "    ('sampling', SMOTE()), \n",
    "    ('classifier', LogisticRegression(solver='liblinear'))])\n",
    "\n",
    "pipe.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score:  0.8726315789473684\n",
      "Best params: {'classifier__C': 0.001, 'classifier__penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'classifier__penalty': [\"l1\",\"l2\"],\n",
    "        'classifier__C': [0.001, 0.01, 0.1, 1, 10, 100]\n",
    "        }\n",
    "grid = GridSearchCV(pipe, param_grid, cv=10, scoring = 'recall', n_jobs=-1)\n",
    "grid.fit(X_train, y_train)\n",
    "print('Best cross-validation score: ', grid.best_score_)\n",
    "print('Best params:' , grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also use the `Pipeline` to test which sampling method gives the best performance (according to the scoring method we choose):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross-validation score:  0.9186842105263159\n",
      "Best params: {'classifier': LogisticRegression(solver='liblinear'), 'classifier__C': 0.001, 'classifier__penalty': 'l2', 'sampling': RandomUnderSampler()}\n"
     ]
    }
   ],
   "source": [
    "pipe = Pipeline([\n",
    "    ('scaling', StandardScaler()), \n",
    "    ('sampling', RandomOverSampler()),\n",
    "    ('classifier', LogisticRegression(solver='liblinear'))])\n",
    "\n",
    "param_grid = [\n",
    "    {'classifier': [LogisticRegression(solver='liblinear')], \n",
    "     'sampling': [None, RandomUnderSampler(), RandomOverSampler(), SMOTE()],\n",
    "     'classifier__penalty': [\"l1\",\"l2\"], \n",
    "     'classifier__C':  [0.001, 0.01, 0.1, 1, 10, 100]}]\n",
    "\n",
    "grid = GridSearchCV(pipe, param_grid, cv=10, scoring = 'recall', n_jobs=-1)\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "print('Best cross-validation score: ', grid.best_score_)\n",
    "print('Best params:' , grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adjusting weights of the minority class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Class imbalance often leads to models that are biased towards the majority class, as the learning process may prioritize minimizing errors on the dominant class(es). By adjusting the weights, models can give more importance to the minority class, encouraging the model to pay more attention to these instances during the training process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In XGBoost a parameter that adjusts class weighting is `scale_pos_weight`.  Typically, it is set as the ratio of number of instances od negative class to the positive class:\n",
    "\n",
    "`scale_pos_weight`=$\\frac{\\text{Number of Negative Samples}}{\\text{Number of Positive Samples}}$\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an illustrative examples using a toy imbalanced dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples=10000, n_features=20, n_classes=2, \n",
    "                           weights=[0.95, 0.05], random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the class distribution in the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.946\n",
       "1    0.054\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y).value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use stratified split to ensure that both the training and the test set maintain the same proportion of classes as in the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will confirm that the class distribution stays the same after the split in both training and the test dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.946\n",
       "1    0.054\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_train).value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.946\n",
       "1    0.054\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(y_test).value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, sinc ethe goal is just to illustrate the use of the weighing parameter, we will not tune the model, though in practice, we should always do so. First we will train an ordinary xgboost that weighs both classes the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-9 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-9 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-9 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-9 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_default = XGBClassifier( random_state=42)\n",
    "model_default.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will set the weight as the ratio of data points in class 0 and the datapoints of class 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.51851851851852"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale_pos_weight = np.sum(y_train == 0) / np.sum(y_train == 1)\n",
    "scale_pos_weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, the `scale_pos_weight` hyperparameter is set to the value of 1 and has the effect of weighing the balance of positive examples, relative to negative examples when boosting decision trees. Next, we train another XGboost model, but this time specifying weight higher than 1 for positive class, which increases the penalty for misclassifying this class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-10 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-10 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-10 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-10 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>XGBClassifier</div></div><div><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "              num_parallel_tree=None, random_state=42, ...)"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_weighted = XGBClassifier( scale_pos_weight=scale_pos_weight, random_state=42)\n",
    "model_weighted.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's evaluate both of these models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model without scale_pos_weight:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98      1892\n",
      "           1       0.69      0.46      0.56       108\n",
      "\n",
      "    accuracy                           0.96      2000\n",
      "   macro avg       0.83      0.73      0.77      2000\n",
      "weighted avg       0.96      0.96      0.96      2000\n",
      "\n",
      "Model with scale_pos_weight:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.98      0.98      1892\n",
      "           1       0.59      0.56      0.57       108\n",
      "\n",
      "    accuracy                           0.95      2000\n",
      "   macro avg       0.78      0.77      0.77      2000\n",
      "weighted avg       0.95      0.95      0.95      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_default = model_default.predict(X_test)\n",
    "y_pred_weighted = model_weighted.predict(X_test)\n",
    "\n",
    "print(\"Model without scale_pos_weight:\")\n",
    "print(classification_report(y_test, y_pred_default))\n",
    "\n",
    "print(\"Model with scale_pos_weight:\")\n",
    "print(classification_report(y_test, y_pred_weighted))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both models preform really well on this toy problem, but the weighted model (with scale_pos_weight) slightly improves recall, 0.56 compared to 0.46 for the minority class (fewer false negatives).\n",
    "However, it also slightly reduced precision, since the model is more sensitive to positive cases, and the precision falls from 0.69 to 0.59."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This heuristic for setting the scale_pos_weight is effective for many situations. However, it is possible that better performance can be achieved with a different class weighting, and this too will depend on the choice of performance metric used to evaluate the model. Hence, the value of this hyperparameter should be tuned with cross validation."
   ]
  }
 ],
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